Data in the Cloud Crash Course
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Santhosh B Gowda presents on Cloudbreak, a tool for provisioning Hadoop clusters on cloud infrastructure. Cloudbreak allows for simplified cluster provisioning through prescriptive setups and automation. It supports declarative workload provisioning across multiple cloud providers with flexible topologies and security configuration options. Cloudbreak also enables features like auto-scaling, recipes to customize clusters, and shared services data lakes to provide common metadata and access management across ephemeral clusters. Demonstrations of launching HDP and HDF clusters from the Cloudbreak UI and CLI are also provided.
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Containers and Big Data
As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Hybrid is the New Normal
It’s becoming clear that enterprises need more than one cloud. Hybrid enables enterprises to optimize how their business works – public cloud for elasticity and scale, multi-cloud for redundancy and choice, and on-premises for performance and privacy. Cloudera delivers a hybrid cloud solution that works where enterprises work, with the agility, security and governance enterprise IT needs, and the self-service analytics business people and enterprise data professionals demand. In this session, we will talk about how Cloudera helps deliver hybrid solutions for enterprises and will run a hands-on Cloudera PaaS demo to exhibit:
- Altus Environment Setup
- Configure Altus SDX
- Spin-up transient clusters with Altus
- Execute workload on Altus Data Engineering clusters
- Run interactive queries on object store with Altus Data Warehouse
- Job Analytics with Workload Experience Manager (WXM)
Speaker: Junaid Rao, Senior Cloud Sales Engineer, Cloudera
Running Enterprise Workloads with an Open Source Hybrid Cloud Data Architecture
Cloud is turbocharging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
With the convergence of cloud, IoT, and big data technologies, enterprises increasingly have their data spread across multiple data lakes on-prem and in cloud data lake stores in many geographies and across multiple public cloud vendor platforms, for example, due to regulatory and compliance mandates that limit cross-border data transfer. With the proliferation of data types and sources in this complex landscape, the process of discovery, provisioning, and running relevant workloads on this data to gather insights has become more complex. Additionally, gaining global visibility into the business context, usage, and trustworthiness of data requires a centralized view of all data and metadata, security controls, data access, and monitoring.
All of these challenges create a significant chasm between initial data capture and subsequent data insights generation to drive value creation. Therefore, enterprises now require a “global insight fabric” that can find a happy medium between adequate rules and policies of data governance while providing a trusted environment for users to collaborate and share data responsibly in order to create value.
In this talk, we will outline how Hortonworks DataPlane Service(DPS) can help customers build a global insight fabric that can span storing and analyzing data within data centers to implementing an open source hybrid architecture that takes advantage of cloud's elasticity and new use cases. We will get a personal view of the challenges faced in safely moving data from on-premises data centers into multiple public clouds, safeguarding it through replication, and then applying consistent security and governance policies across diverse environments to deliver trusted data and insights to the business. We will highlight how DataPlane Service can help enterprises with this hybrid architectural journey, and how open source architectures are enabling this transformation across enterprises.
Speaker: Alan Gates, Co-Founder, Hortonworks
Ozone and HDFS's Evolution
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Deep learning 101
In this talk, we will give an overview of the deep learning space starting with a brief history. We will distinguish between deep learning hype vs practical real-world applications, cover how deep learning differs from other machine learning algorithms, go over sample neural net architectures, and provide a step-by-step guide on how to get started.
Specifically, we will cover what type of training data is required and how to prepare it with Apache Spark, followed by how to choose a correct neural net architecture, train, and deploy a deep learning model with TensorFlow on Apache Hadoop 3.1.
Finally, we will wrap-up with deep learning challenges and shortcomings, and offer short- and long-term recommendations to successfully train and deploy deep learning models within your organization to maximize return on investment.
Data in the Cloud Crash Course
Introduction
This workshop is a hands-on session to quickly deploy Hadoop and Streaming on AWS / Azure / Google Cloud.
Cloudbreak simplifies the deployment of Hadoop in cloud environments. It enables the enterprise to quickly run big data workloads in the cloud while optimizing the use of cloud resources.
Format
A short introductory lecture about Cloudbreak. This is followed by a walk through and lab leveraging Hadoop and Streaming in the Cloud with Cloudbreak.
Objective
To provide a quick and short hands-on introduction to Hadoop on the cloud. Review key benefits of cluster deployment automation.
This lab will use Cloudbreak to quickly and effortlessly stand up Hadoop and Streaming clusters in a cloud provider of your choice. The lab shows the use of Ambari blueprints that are your declarative definitions of your Hadoop or Streaming clusters. Steps to dynamically change these blueprints and use external databases and external authentication sources and in essence showing a way to provide Shared Authentication, Authorization and Audit across ephemeral and long-lasting clusters. However it is not limited to only custom blueprints, the lab also shows how Cloudbreak provides easy to use custom scripts called recipes that can be executed before or after Ambari start or after cluster installation.
Keynote
1. Sanjay Radia discusses designing data systems for data everywhere, including on-premises and in the cloud.
2. He advocates for applying a consistent set of tools, managing data in one place, and sharing security and governance across systems.
3. Radia also notes the importance of leveraging innovation from open source communities to continually improve the systems.
Curing the Kafka Blindness – Streams Messaging Manager
Companies who use Kafka today struggle with monitoring and managing Kafka clusters. Kafka is a key backbone of IoT streaming analytics applications. The challenge is understanding what is going on overall in the Kafka cluster including performance, issues and message flows. No open source tool caters to the needs of different users that work with Kafka: DevOps/developers, platform team, and security/governance teams. See how the new Hortonworks Streams Messaging Manager enables users to visualize their entire Kafka environment end-to-end and simplifies Kafka operations.
In this session learn how SMM visualizes the intricate details of how Apache Kafka functions in real time while simultaneously surfacing every nuance of tuning, optimizing, and measuring input and output. SMM will assist users to quickly understand and operate Kafka while providing the much-needed transparency that sophisticated and experienced users need to avoid all the pitfalls of running a Kafka cluster.
Speaker: Andrew Psaltis, Principal Solution Engineer, Hortonworks
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Containers and Big Data
As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Hybrid is the New Normal
It’s becoming clear that enterprises need more than one cloud. Hybrid enables enterprises to optimize how their business works – public cloud for elasticity and scale, multi-cloud for redundancy and choice, and on-premises for performance and privacy. Cloudera delivers a hybrid cloud solution that works where enterprises work, with the agility, security and governance enterprise IT needs, and the self-service analytics business people and enterprise data professionals demand. In this session, we will talk about how Cloudera helps deliver hybrid solutions for enterprises and will run a hands-on Cloudera PaaS demo to exhibit:
- Altus Environment Setup
- Configure Altus SDX
- Spin-up transient clusters with Altus
- Execute workload on Altus Data Engineering clusters
- Run interactive queries on object store with Altus Data Warehouse
- Job Analytics with Workload Experience Manager (WXM)
Speaker: Junaid Rao, Senior Cloud Sales Engineer, Cloudera
Running Enterprise Workloads with an Open Source Hybrid Cloud Data Architecture
Cloud is turbocharging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
With the convergence of cloud, IoT, and big data technologies, enterprises increasingly have their data spread across multiple data lakes on-prem and in cloud data lake stores in many geographies and across multiple public cloud vendor platforms, for example, due to regulatory and compliance mandates that limit cross-border data transfer. With the proliferation of data types and sources in this complex landscape, the process of discovery, provisioning, and running relevant workloads on this data to gather insights has become more complex. Additionally, gaining global visibility into the business context, usage, and trustworthiness of data requires a centralized view of all data and metadata, security controls, data access, and monitoring.
All of these challenges create a significant chasm between initial data capture and subsequent data insights generation to drive value creation. Therefore, enterprises now require a “global insight fabric” that can find a happy medium between adequate rules and policies of data governance while providing a trusted environment for users to collaborate and share data responsibly in order to create value.
In this talk, we will outline how Hortonworks DataPlane Service(DPS) can help customers build a global insight fabric that can span storing and analyzing data within data centers to implementing an open source hybrid architecture that takes advantage of cloud's elasticity and new use cases. We will get a personal view of the challenges faced in safely moving data from on-premises data centers into multiple public clouds, safeguarding it through replication, and then applying consistent security and governance policies across diverse environments to deliver trusted data and insights to the business. We will highlight how DataPlane Service can help enterprises with this hybrid architectural journey, and how open source architectures are enabling this transformation across enterprises.
Speaker: Alan Gates, Co-Founder, Hortonworks
Ozone and HDFS's Evolution
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Deep learning 101
In this talk, we will give an overview of the deep learning space starting with a brief history. We will distinguish between deep learning hype vs practical real-world applications, cover how deep learning differs from other machine learning algorithms, go over sample neural net architectures, and provide a step-by-step guide on how to get started.
Specifically, we will cover what type of training data is required and how to prepare it with Apache Spark, followed by how to choose a correct neural net architecture, train, and deploy a deep learning model with TensorFlow on Apache Hadoop 3.1.
Finally, we will wrap-up with deep learning challenges and shortcomings, and offer short- and long-term recommendations to successfully train and deploy deep learning models within your organization to maximize return on investment.
Data in the Cloud Crash Course
Introduction
This workshop is a hands-on session to quickly deploy Hadoop and Streaming on AWS / Azure / Google Cloud.
Cloudbreak simplifies the deployment of Hadoop in cloud environments. It enables the enterprise to quickly run big data workloads in the cloud while optimizing the use of cloud resources.
Format
A short introductory lecture about Cloudbreak. This is followed by a walk through and lab leveraging Hadoop and Streaming in the Cloud with Cloudbreak.
Objective
To provide a quick and short hands-on introduction to Hadoop on the cloud. Review key benefits of cluster deployment automation.
This lab will use Cloudbreak to quickly and effortlessly stand up Hadoop and Streaming clusters in a cloud provider of your choice. The lab shows the use of Ambari blueprints that are your declarative definitions of your Hadoop or Streaming clusters. Steps to dynamically change these blueprints and use external databases and external authentication sources and in essence showing a way to provide Shared Authentication, Authorization and Audit across ephemeral and long-lasting clusters. However it is not limited to only custom blueprints, the lab also shows how Cloudbreak provides easy to use custom scripts called recipes that can be executed before or after Ambari start or after cluster installation.
Keynote
1. Sanjay Radia discusses designing data systems for data everywhere, including on-premises and in the cloud.
2. He advocates for applying a consistent set of tools, managing data in one place, and sharing security and governance across systems.
3. Radia also notes the importance of leveraging innovation from open source communities to continually improve the systems.
Curing the Kafka Blindness – Streams Messaging Manager
Companies who use Kafka today struggle with monitoring and managing Kafka clusters. Kafka is a key backbone of IoT streaming analytics applications. The challenge is understanding what is going on overall in the Kafka cluster including performance, issues and message flows. No open source tool caters to the needs of different users that work with Kafka: DevOps/developers, platform team, and security/governance teams. See how the new Hortonworks Streams Messaging Manager enables users to visualize their entire Kafka environment end-to-end and simplifies Kafka operations.
In this session learn how SMM visualizes the intricate details of how Apache Kafka functions in real time while simultaneously surfacing every nuance of tuning, optimizing, and measuring input and output. SMM will assist users to quickly understand and operate Kafka while providing the much-needed transparency that sophisticated and experienced users need to avoid all the pitfalls of running a Kafka cluster.
Speaker: Andrew Psaltis, Principal Solution Engineer, Hortonworks
Fast SQL on Hadoop, really?
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
10 Lessons Learned from Meeting with 150 Banks Across the Globe
This document summarizes 10 practical lessons learned from companies about their big data and analytics journeys:
1. There are clear leaders in each market who are gaining substantial benefits from using big data and machine learning, widening the gap with other companies.
2. Real transformation requires buy-in from top executives, as reflected by new innovation centers, roles, and organizations.
3. Projects should have clear revenue impact objectives and be selected based on estimated return, with pre- and post-implementation measurements.
4. While cost reduction brings the fastest ROI, new revenue opportunities can transform a business more lastingly if the projects address real customer and business needs.
What is New in Apache Hive 3.0?
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
Speaker: Alan Gates, Co-Founder, Hortonworks
Sharing metadata across the data lake and streams
Traditionally systems have stored and managed their own metadata, just as they traditionally stored and managed their own data. A revolutionary feature of big data tools such as Apache Hadoop and Apache Kafka is the ability to store all data together, where users can bring the tools of their choice to process it.
Apache Hive's metastore can be used to share the metadata in the same way. It is already used by many SQL and SQL-like systems beyond Hive (e.g. Apache Spark, Presto, Apache Impala, and via HCatalog, Apache Pig). As data processing changes from only data in the cluster to include data in streams, the metastore needs to expand and grow to meet these use cases as well. There is work going on in the Hive community to separate out the metastore, so it can continue to serve Hive but also be used by a more diverse set of tools. This talk will discuss that work, with particular focus on adding support for storing schemas for Kafka messages.
Speaker
Alan Gates, Co-Founder, Hortonworks
Delivering a Flexible IT Infrastructure for Analytics on IBM Power Systems
Customers are preparing themselves to analyze and manage an increasing quantity of structured and unstructured data. Business leaders introduce new analytical workloads faster than what IT departments can handle. Legacy IT infrastructure needs to evolve to deliver operational improvements and cost containment, while increasing flexibility to meet future requirements. By providing HDP on IBM Power Systems, Hortonworks and IBM are giving customers have more choice in selecting the appropriate architectural platform that is right for them. In this webinar, we’ll discuss some of the challenges with deploying big data platforms, and how choosing solutions built with HDP on IBM Power Systems can offer tangible benefits and flexibility to accommodate changing needs.
What's New in Apache Hive 3.0?
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Lessons learned running a container cloud on YARN
Apache Hadoop YARN is the resource and application manager for Apache Hadoop. In the past, YARN only supported launching containers as processes. However, as containerization has become extremely popular, more and more users wanted support for launching Docker containers. With recent changes, YARN now supports running Docker containers alongside process containers. Coupled with the newly added support for long-running services on YARN, this allows a host of new possibilities.
In this talk, we'll present how to run a container cloud on YARN. Leveraging the support in YARN for Docker and long-running services, we can allow users to easily spin up sets of Docker containers for their applications. These containers can be self contained or wired up to form more complex applications. We will go over some of the lessons we learned as part of our experiences handling issues such as resource management, debugging application failures, running Docker, service discovery, etc.
Speaker
Billie Rinaldi, Principal Software Engineer I, Hortonworks
Using Spark Streaming and NiFi for the Next Generation of ETL in the Enterprise
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speaker: Andrew Psaltis, Principal Solution Engineer, Hortonworks
Containers and Big Data
As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN.
Ozone and HDFS’s evolution
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Apache Hadoop YARN: state of the union
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Deep learning on yarn running distributed tensorflow etc on hadoop cluster v3
Deep learning is useful for enterprises tasks in the field of speech recognition, image classification, AI chatbots, and machine translation, just to name a few.
In order to train deep learning/machine learning models, applications such as TensorFlow, MXNet, Caffe, and XGBoost can be leveraged. And sometimes these applications will be used together to solve different problems.
To make distributed deep learning/machine learning applications easily launched, managed, and monitored, we introduced, in Apache Hadoop 3.x, YARN native services along with other improvements such as first-class GPU support, container-DNS support, scheduling improvements, etc. These improvements make distributed deep learning/machine learning applications run on YARN as simple as running it locally, which can let machine learning engineers focus on algorithms instead of worrying about underlying infrastructure. Also, YARN can better manage a shared cluster which runs deep learning/machine learning and other services and ETL jobs with these improvements.
In this session, we will take a closer look at these improvements and show how to run these applications on YARN with demos. Audiences can start trying running these applications on YARN after this talk.
Speakers
Wanga Tan, Staff Software Engineer, Hortonworks
Sunil Govindan, Staff Engineer, Hortonworks
Hadoop Operations – Past, Present, and Future
Hadoop operations started on-prem primarily driven by Apache Ambari. However, due to the agility and flexibility of the cloud, it has driven many Hadoop cluster operations to the cloud and to hybrid environments. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
Apache Ambari is used by thousands of Hadoop Operators to manage the deployment, lifecycle, and automation of DevOps for Hadoop ecosystem projects. Starting out, Apache Ambari installed a handful of Apache Hadoop ecosystem projects, on a few operating systems, and helped with the most basic Hadoop operational tasks. Today, the product manages over 20 different services, runs on multiple major operating systems and versions, and automates many of the most challenging Hadoop operational tasks in the most secure customer environments.
In this session, we will also take you through Cloudbreak as a solution to simplify provisioning and managing enterprise workloads while providing an open and common experience for deploying workloads across clouds. We will discuss the challenges (and opportunities) to run enterprise workloads in the cloud and will go through a live demo of how the latest from Cloudbreak enables enterprises to easily and securely run Apache Hadoop. This includes deep-dive discussion on Ambari Blueprints, recipes, custom images, and enabling Kerberos -- which are all key capabilities for Enterprise deployments.
As part of this talk, will walk you through what we've learned, the challenges we've overcome, and how the Apache Ambari and Cloudbreak community has changed the product to handle them. The future is fast approaching, and with it comes new on-premise and cloud deployment architectures. See how Apache Ambari and Cloudbreak are being re-imagined to handle these new challenges.
Speaker: Santosh Gowda, Principal Solutions Engineer, Hortonworks
Hadoop: The Unintended Benefits
The document discusses some unintended benefits that the Department of Home Affairs discovered from implementing Hadoop. It describes how Hadoop was initially used for passenger systems and the Teradata data warehouse, but then enabled additional uses. Solr was used to enable fast search of billions of rows across Hadoop, improving on slow full table scans in Teradata. A graph database called JanusGraph was also implemented on Hadoop to enable relationship analysis and predictive analytics for security use cases. The document outlines the architectures used and lessons learned around performance, security, and managing expectations for these new capabilities enabled by Hadoop.
The First Mile – Edge and IoT Data Collection with Apache NiFi and MiNiFi
Apache NiFi MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately.
Abstract: Apache NiFi provided a revolutionary data flow management system with a broad range of integrations with existing data production, consumption, and analysis ecosystems, all covered with robust data delivery and provenance infrastructure. Now learn about the follow-on project which expands the reach of NiFi to the edge, Apache MiNiFi. MiNiFi is a lightweight application which can be deployed on hardware orders of magnitude smaller and less powerful than the existing standard data collection platforms. With both a JVM compatible and native agent, MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately. Local governance and regulatory policies can be applied across geopolitical boundaries to conform with legal requirements. And all of this configuration can be done from central command & control using an existing NiFi with the trusted and stable UI data flow managers already love.
Expected prior knowledge / intended audience: developers and data flow managers should have a passing knowledge of Apache NiFi as a platform for routing, transforming, and delivering data through systems (a brief overview will be provided). The talk will focus on extending the data collection, routing, provenance, and governance capabilities of NiFi to IoT/edge integration via MiNiFi.
Takeaways: Attendees will learn about opportunities to bring their data flow and capture closer to the "edge" -- sources of data like IoT devices, vehicles, machinery, etc. They will understand the possibilities to prioritize, filter, secure, and manipulate this data earlier in the data lifecycle to enhance their data visibility and performance.
Speaker: Andy LoPresto, Sr. Member of Technical Staff, Hortonworks
Apache Hadoop YARN: state of the union - Tokyo
Apache Hadoop YARN is the latest distributed operating system for HDSF for big data applications and storage. YARN has transformed the Hadoop Compute Layer into a general resource management platform capable of hosting a wide variety of applications.
This lecture begins with the current state how Apache Hadoop YARN is currently used in large scale deployment. The next topic will cover about strengthening YARN 's current and future - like YARN' s excitement - as a top-notch resource management platform for data centers running enterprise Hadoop. Discuss the current state and future of the following functions and initiatives: support of machine learning through strong container placement, global scheduling, GPU and FPGA support and deep learning workload, large scale of YARN federation, on YARN Containerized applications, natural support that does not change to long-running services (along with applications), seamless application upgrades, powerful scheduling functions, operational improvements and better queue management.
The second part of the lecture focuses on the latest enhancement of HDFS. HDFS has several advantages: horizontal scale of IO bandwidth, storage scaled to petabyte storage. In addition, it provides extremely low latency metadata operations and coordinates for over 60,000 concurrent clients. Hadoop 3.0 recently introduced Erasure Coding. One limitation of HDFS is the scaling of multiple files and blocks in the system. I will explain the fundamental change of Hadoop's storage infrastructure using Ozone technology which will be announced soon. This will allow Hadoop to scale billions of files and blocks in the future to a larger number of smaller objects than before.
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Companies in every industry look for ways to explore new data types and large data sets that were previously too big to capture, store and process. They need to unlock insights from data such as clickstream, geo-location, sensor, server log, social, text and video data. However, becoming a data-first enterprise comes with many challenges.
Join this webinar organized by three leaders in their respective fields and learn from our experts how you can accelerate the implementation of a scalable, cost-efficient and robust Big Data solution. Cisco, Hortonworks and Red Hat will explore how new data sets can enrich existing analytic applications with new perspectives and insights and how they can help you drive the creation of innovative new apps that provide new value to your business.
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Overview of Apache Hive 3 performance, with description of LLAP, caching, materialized views, and integration with Apache Druid.
Driving in the Desert - Running Your HDP Cluster with Helion, Openstack, and ...
DataWorks Summit 2017 - Sydney
Alejandro Tesch, Cloud Evangelist, Asia Pacific and Japan, HPE
Big Data is a hot topic today for most organisations today as they race to convert vast amounts of data into useful information that can be leveraged to make critical decisions and recommendations in a very limited time windows. Today, there is a widely accepted talent gap when it comes to creating and managing Hadoop cluster, even for the experts – it can take hours (or days) to get a fully functional hadoop farm up and running. The HDP Ambari plugin for Sahara is looking to address most of this challenges by facilitating the deployment of Hortonworks Hadoop clusters and provide a set of open API to facilitate data analytics tasks in your own cloud. In this presentation we will cover why it makes sense to run your data analytics cluster in your cloud and we will demonstrate basic Sahara / Ambari functionality.
Insights into Real-world Data Management Challenges
Oracle began with the belief that the foundation of IT was managing information. The Oracle Cloud Platform for Big Data is a natural extension of our belief in the power of data. Oracle’s Integrated Cloud is one cloud for the entire business, meeting everyone’s needs. It’s about Connecting people to information through tools which help you combine and aggregate data from any source.
This session will explore how organizations can transition to the cloud by delivering fully managed and elastic Hadoop and Real-time Streaming cloud services to built robust offerings that provide measurable value to the business. We will explore key data management trends and dive deeper into pain points we are hearing about from our customer base.
Running Enterprise Workloads in the Cloud
Demand for cloud is through the roof. Cloud is turbo charging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
In this session, we will take you through Cloudbreak as a solution to simplify provisioning and managing enterprise workloads while providing an open and common experience for deploying workloads across clouds. We will discuss the challenges (and opportunities) to run enterprise workloads in the cloud and will go through how the latest from Cloudbreak enables enterprises to easily and securely run big data workloads. This includes deep-dive discussion on autoscaling, Ambari Blueprints, recipes, custom images, and enabling Kerberos -- which are all key capabilities for Enterprise deployments.
As a last topic we will discuss how we deployed and operate Cloudbreak as a Service internally which enables rapid cluster deployment for prototyping and testing purposes.
Speakers
Peter Darvasi, Cloudbreak Partner Engineer, Hortonworks
Richard Doktorics, Staff Engineer, Hortonworks
Containers and Big Data
As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN. BILLIE RINALDI, Principal Software Engineer, Hortonworks and SHANE KUMPF, Software Engineer, Hortonworks
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Fast SQL on Hadoop, really?
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
10 Lessons Learned from Meeting with 150 Banks Across the Globe
This document summarizes 10 practical lessons learned from companies about their big data and analytics journeys:
1. There are clear leaders in each market who are gaining substantial benefits from using big data and machine learning, widening the gap with other companies.
2. Real transformation requires buy-in from top executives, as reflected by new innovation centers, roles, and organizations.
3. Projects should have clear revenue impact objectives and be selected based on estimated return, with pre- and post-implementation measurements.
4. While cost reduction brings the fastest ROI, new revenue opportunities can transform a business more lastingly if the projects address real customer and business needs.
What is New in Apache Hive 3.0?
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
Speaker: Alan Gates, Co-Founder, Hortonworks
Sharing metadata across the data lake and streams
Traditionally systems have stored and managed their own metadata, just as they traditionally stored and managed their own data. A revolutionary feature of big data tools such as Apache Hadoop and Apache Kafka is the ability to store all data together, where users can bring the tools of their choice to process it.
Apache Hive's metastore can be used to share the metadata in the same way. It is already used by many SQL and SQL-like systems beyond Hive (e.g. Apache Spark, Presto, Apache Impala, and via HCatalog, Apache Pig). As data processing changes from only data in the cluster to include data in streams, the metastore needs to expand and grow to meet these use cases as well. There is work going on in the Hive community to separate out the metastore, so it can continue to serve Hive but also be used by a more diverse set of tools. This talk will discuss that work, with particular focus on adding support for storing schemas for Kafka messages.
Speaker
Alan Gates, Co-Founder, Hortonworks
Delivering a Flexible IT Infrastructure for Analytics on IBM Power Systems
Customers are preparing themselves to analyze and manage an increasing quantity of structured and unstructured data. Business leaders introduce new analytical workloads faster than what IT departments can handle. Legacy IT infrastructure needs to evolve to deliver operational improvements and cost containment, while increasing flexibility to meet future requirements. By providing HDP on IBM Power Systems, Hortonworks and IBM are giving customers have more choice in selecting the appropriate architectural platform that is right for them. In this webinar, we’ll discuss some of the challenges with deploying big data platforms, and how choosing solutions built with HDP on IBM Power Systems can offer tangible benefits and flexibility to accommodate changing needs.
What's New in Apache Hive 3.0?
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Lessons learned running a container cloud on YARN
Apache Hadoop YARN is the resource and application manager for Apache Hadoop. In the past, YARN only supported launching containers as processes. However, as containerization has become extremely popular, more and more users wanted support for launching Docker containers. With recent changes, YARN now supports running Docker containers alongside process containers. Coupled with the newly added support for long-running services on YARN, this allows a host of new possibilities.
In this talk, we'll present how to run a container cloud on YARN. Leveraging the support in YARN for Docker and long-running services, we can allow users to easily spin up sets of Docker containers for their applications. These containers can be self contained or wired up to form more complex applications. We will go over some of the lessons we learned as part of our experiences handling issues such as resource management, debugging application failures, running Docker, service discovery, etc.
Speaker
Billie Rinaldi, Principal Software Engineer I, Hortonworks
Using Spark Streaming and NiFi for the Next Generation of ETL in the Enterprise
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speaker: Andrew Psaltis, Principal Solution Engineer, Hortonworks
Containers and Big Data
As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN.
Ozone and HDFS’s evolution
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Apache Hadoop YARN: state of the union
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Deep learning on yarn running distributed tensorflow etc on hadoop cluster v3
Deep learning is useful for enterprises tasks in the field of speech recognition, image classification, AI chatbots, and machine translation, just to name a few.
In order to train deep learning/machine learning models, applications such as TensorFlow, MXNet, Caffe, and XGBoost can be leveraged. And sometimes these applications will be used together to solve different problems.
To make distributed deep learning/machine learning applications easily launched, managed, and monitored, we introduced, in Apache Hadoop 3.x, YARN native services along with other improvements such as first-class GPU support, container-DNS support, scheduling improvements, etc. These improvements make distributed deep learning/machine learning applications run on YARN as simple as running it locally, which can let machine learning engineers focus on algorithms instead of worrying about underlying infrastructure. Also, YARN can better manage a shared cluster which runs deep learning/machine learning and other services and ETL jobs with these improvements.
In this session, we will take a closer look at these improvements and show how to run these applications on YARN with demos. Audiences can start trying running these applications on YARN after this talk.
Speakers
Wanga Tan, Staff Software Engineer, Hortonworks
Sunil Govindan, Staff Engineer, Hortonworks
Hadoop Operations – Past, Present, and Future
Hadoop operations started on-prem primarily driven by Apache Ambari. However, due to the agility and flexibility of the cloud, it has driven many Hadoop cluster operations to the cloud and to hybrid environments. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
Apache Ambari is used by thousands of Hadoop Operators to manage the deployment, lifecycle, and automation of DevOps for Hadoop ecosystem projects. Starting out, Apache Ambari installed a handful of Apache Hadoop ecosystem projects, on a few operating systems, and helped with the most basic Hadoop operational tasks. Today, the product manages over 20 different services, runs on multiple major operating systems and versions, and automates many of the most challenging Hadoop operational tasks in the most secure customer environments.
In this session, we will also take you through Cloudbreak as a solution to simplify provisioning and managing enterprise workloads while providing an open and common experience for deploying workloads across clouds. We will discuss the challenges (and opportunities) to run enterprise workloads in the cloud and will go through a live demo of how the latest from Cloudbreak enables enterprises to easily and securely run Apache Hadoop. This includes deep-dive discussion on Ambari Blueprints, recipes, custom images, and enabling Kerberos -- which are all key capabilities for Enterprise deployments.
As part of this talk, will walk you through what we've learned, the challenges we've overcome, and how the Apache Ambari and Cloudbreak community has changed the product to handle them. The future is fast approaching, and with it comes new on-premise and cloud deployment architectures. See how Apache Ambari and Cloudbreak are being re-imagined to handle these new challenges.
Speaker: Santosh Gowda, Principal Solutions Engineer, Hortonworks
Hadoop: The Unintended Benefits
The document discusses some unintended benefits that the Department of Home Affairs discovered from implementing Hadoop. It describes how Hadoop was initially used for passenger systems and the Teradata data warehouse, but then enabled additional uses. Solr was used to enable fast search of billions of rows across Hadoop, improving on slow full table scans in Teradata. A graph database called JanusGraph was also implemented on Hadoop to enable relationship analysis and predictive analytics for security use cases. The document outlines the architectures used and lessons learned around performance, security, and managing expectations for these new capabilities enabled by Hadoop.
The First Mile – Edge and IoT Data Collection with Apache NiFi and MiNiFi
Apache NiFi MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately.
Abstract: Apache NiFi provided a revolutionary data flow management system with a broad range of integrations with existing data production, consumption, and analysis ecosystems, all covered with robust data delivery and provenance infrastructure. Now learn about the follow-on project which expands the reach of NiFi to the edge, Apache MiNiFi. MiNiFi is a lightweight application which can be deployed on hardware orders of magnitude smaller and less powerful than the existing standard data collection platforms. With both a JVM compatible and native agent, MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately. Local governance and regulatory policies can be applied across geopolitical boundaries to conform with legal requirements. And all of this configuration can be done from central command & control using an existing NiFi with the trusted and stable UI data flow managers already love.
Expected prior knowledge / intended audience: developers and data flow managers should have a passing knowledge of Apache NiFi as a platform for routing, transforming, and delivering data through systems (a brief overview will be provided). The talk will focus on extending the data collection, routing, provenance, and governance capabilities of NiFi to IoT/edge integration via MiNiFi.
Takeaways: Attendees will learn about opportunities to bring their data flow and capture closer to the "edge" -- sources of data like IoT devices, vehicles, machinery, etc. They will understand the possibilities to prioritize, filter, secure, and manipulate this data earlier in the data lifecycle to enhance their data visibility and performance.
Speaker: Andy LoPresto, Sr. Member of Technical Staff, Hortonworks
Apache Hadoop YARN: state of the union - Tokyo
Apache Hadoop YARN is the latest distributed operating system for HDSF for big data applications and storage. YARN has transformed the Hadoop Compute Layer into a general resource management platform capable of hosting a wide variety of applications.
This lecture begins with the current state how Apache Hadoop YARN is currently used in large scale deployment. The next topic will cover about strengthening YARN 's current and future - like YARN' s excitement - as a top-notch resource management platform for data centers running enterprise Hadoop. Discuss the current state and future of the following functions and initiatives: support of machine learning through strong container placement, global scheduling, GPU and FPGA support and deep learning workload, large scale of YARN federation, on YARN Containerized applications, natural support that does not change to long-running services (along with applications), seamless application upgrades, powerful scheduling functions, operational improvements and better queue management.
The second part of the lecture focuses on the latest enhancement of HDFS. HDFS has several advantages: horizontal scale of IO bandwidth, storage scaled to petabyte storage. In addition, it provides extremely low latency metadata operations and coordinates for over 60,000 concurrent clients. Hadoop 3.0 recently introduced Erasure Coding. One limitation of HDFS is the scaling of multiple files and blocks in the system. I will explain the fundamental change of Hadoop's storage infrastructure using Ozone technology which will be announced soon. This will allow Hadoop to scale billions of files and blocks in the future to a larger number of smaller objects than before.
A Comprehensive Approach to Building your Big Data - with Cisco, Hortonworks ...
Companies in every industry look for ways to explore new data types and large data sets that were previously too big to capture, store and process. They need to unlock insights from data such as clickstream, geo-location, sensor, server log, social, text and video data. However, becoming a data-first enterprise comes with many challenges.
Join this webinar organized by three leaders in their respective fields and learn from our experts how you can accelerate the implementation of a scalable, cost-efficient and robust Big Data solution. Cisco, Hortonworks and Red Hat will explore how new data sets can enrich existing analytic applications with new perspectives and insights and how they can help you drive the creation of innovative new apps that provide new value to your business.
Hive Performance Dataworks Summit Melbourne February 2019
Overview of Apache Hive 3 performance, with description of LLAP, caching, materialized views, and integration with Apache Druid.
Driving in the Desert - Running Your HDP Cluster with Helion, Openstack, and ...
DataWorks Summit 2017 - Sydney
Alejandro Tesch, Cloud Evangelist, Asia Pacific and Japan, HPE
Big Data is a hot topic today for most organisations today as they race to convert vast amounts of data into useful information that can be leveraged to make critical decisions and recommendations in a very limited time windows. Today, there is a widely accepted talent gap when it comes to creating and managing Hadoop cluster, even for the experts – it can take hours (or days) to get a fully functional hadoop farm up and running. The HDP Ambari plugin for Sahara is looking to address most of this challenges by facilitating the deployment of Hortonworks Hadoop clusters and provide a set of open API to facilitate data analytics tasks in your own cloud. In this presentation we will cover why it makes sense to run your data analytics cluster in your cloud and we will demonstrate basic Sahara / Ambari functionality.
Insights into Real-world Data Management Challenges
Oracle began with the belief that the foundation of IT was managing information. The Oracle Cloud Platform for Big Data is a natural extension of our belief in the power of data. Oracle’s Integrated Cloud is one cloud for the entire business, meeting everyone’s needs. It’s about Connecting people to information through tools which help you combine and aggregate data from any source.
This session will explore how organizations can transition to the cloud by delivering fully managed and elastic Hadoop and Real-time Streaming cloud services to built robust offerings that provide measurable value to the business. We will explore key data management trends and dive deeper into pain points we are hearing about from our customer base.
La actualidad más candente (20)
10 Lessons Learned from Meeting with 150 Banks Across the Globe
10 Lessons Learned from Meeting with 150 Banks Across the Globe
Delivering a Flexible IT Infrastructure for Analytics on IBM Power Systems
Delivering a Flexible IT Infrastructure for Analytics on IBM Power Systems
Using Spark Streaming and NiFi for the Next Generation of ETL in the Enterprise
Using Spark Streaming and NiFi for the Next Generation of ETL in the Enterprise
Deep learning on yarn running distributed tensorflow etc on hadoop cluster v3
Deep learning on yarn running distributed tensorflow etc on hadoop cluster v3
The First Mile – Edge and IoT Data Collection with Apache NiFi and MiNiFi
The First Mile – Edge and IoT Data Collection with Apache NiFi and MiNiFi
A Comprehensive Approach to Building your Big Data - with Cisco, Hortonworks ...
A Comprehensive Approach to Building your Big Data - with Cisco, Hortonworks ...
Hive Performance Dataworks Summit Melbourne February 2019
Hive Performance Dataworks Summit Melbourne February 2019
Driving in the Desert - Running Your HDP Cluster with Helion, Openstack, and ...
Driving in the Desert - Running Your HDP Cluster with Helion, Openstack, and ...
Insights into Real-world Data Management Challenges
Insights into Real-world Data Management Challenges
Similar a Data in the Cloud Crash Course
Running Enterprise Workloads in the Cloud
Demand for cloud is through the roof. Cloud is turbo charging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
In this session, we will take you through Cloudbreak as a solution to simplify provisioning and managing enterprise workloads while providing an open and common experience for deploying workloads across clouds. We will discuss the challenges (and opportunities) to run enterprise workloads in the cloud and will go through how the latest from Cloudbreak enables enterprises to easily and securely run big data workloads. This includes deep-dive discussion on autoscaling, Ambari Blueprints, recipes, custom images, and enabling Kerberos -- which are all key capabilities for Enterprise deployments.
As a last topic we will discuss how we deployed and operate Cloudbreak as a Service internally which enables rapid cluster deployment for prototyping and testing purposes.
Speakers
Peter Darvasi, Cloudbreak Partner Engineer, Hortonworks
Richard Doktorics, Staff Engineer, Hortonworks
Containers and Big Data
As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN. BILLIE RINALDI, Principal Software Engineer, Hortonworks and SHANE KUMPF, Software Engineer, Hortonworks
Apache Hadoop 3 updates with migration story
The document discusses migrating Hadoop clusters from version 2 to version 3. It provides an overview of new features in HDFS, YARN, and other components in Hadoop 3, including erasure coding in HDFS, global scheduling and new resource types in YARN. It also covers important considerations for upgrading such as recommended source and target versions, upgrade mechanisms, tooling changes, and ensuring Java 8 is used.
Hadoop Operations - Past, Present, and Future
Hadoop operations started on-prem primarily driven by Apache Ambari. However, due to the agility and flexibility of the cloud, it has driven many Hadoop cluster operations to the cloud and to hybrid environments. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
Apache Ambari is used by thousands of Hadoop Operators to manage the deployment, lifecycle, and automation of DevOps for Hadoop ecosystem projects. Starting out, Apache Ambari installed a handful of Apache Hadoop ecosystem projects, on a few operating systems, and helped with the most basic Hadoop operational tasks. Today, the product manages over 20 different services, runs on multiple major operating systems and versions, and automates many of the most challenging Hadoop operational tasks in the most secure customer environments.
In this session, we will also take you through Cloudbreak as a solution to simplify provisioning and managing enterprise workloads while providing an open and common experience for deploying workloads across clouds. We will discuss the challenges (and opportunities) to run enterprise workloads in the cloud and will go through a live demo of how the latest from Cloudbreak enables enterprises to easily and securely run Apache Hadoop. This includes deep-dive discussion on Ambari Blueprints, recipes, custom images, and enabling Kerberos -- which are all key capabilities for Enterprise deployments.
As part of this talk, will walk you through what we've learned, the challenges we've overcome, and how the Apache Ambari and Cloudbreak community has changed the product to handle them. The future is fast approaching, and with it comes new on-premise and cloud deployment architectures. See how Apache Ambari and Cloudbreak are being re-imagined to handle these new challenges.
Saving the elephant—now, not later
Big data security challenges are bit different from traditional client-server applications and are distributed in nature, introducing unique security vulnerabilities. Cloud Security Alliance (CSA) has categorized the different security and privacy challenges into four different aspects of the big data ecosystem. These aspects are infrastructure security, data privacy, data management and, integrity and reactive security. Each of these aspects are further divided into following security challenges:
1. Infrastructure security
a. Secure distributed processing of data
b. Security best practices for non-relational data stores
2. Data privacy
a. Privacy-preserving analytics
b. Cryptographic technologies for big data
c. Granular access control
3. Data management
a. Secure data storage and transaction logs
b. Granular audits
c. Data provenance
4. Integrity and reactive security
a. Endpoint input validation/filtering
b. Real-time security/compliance monitoring
In this talk, we are going to refer above classification and identify existing security controls, best practices, and guidelines. We will also paint a big picture about how collective usage of all discussed security controls (Kerberos, TDE, LDAP, SSO, SSL/TLS, Apache Knox, Apache Ranger, Apache Atlas, Ambari Infra, etc.) can address fundamental security and privacy challenges that encompass the entire Hadoop ecosystem. We will also discuss briefly recent security incidents involving Hadoop systems.
Speakers
Krishna Pandey, Staff Software Engineer, Hortonworks
Kunal Rajguru, Premier Support Engineer, Hortonworks
Fortifying Multi-Cluster Hybrid Cloud Data Lakes using Apache Knox
The document discusses securing multi-cluster hybrid cloud data lakes using Apache Knox. It describes how Knox acts as a reverse proxy and gateway to provide single sign-on, authentication, authorization, and security for clusters spanning on-premises and cloud environments. A demo is presented showing how Knox can authenticate users without moving the on-premises LDAP, restrict unauthorized access, and protect against stolen credentials through features like federation and authorization.
What's new in apache hive
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in big data ecosystem. Although, Hive started primarily as batch ingestion and reporting tool, community is hard at work in improving it along many different dimensions and use cases. This talk will provide an overview of latest and greatest features and optimizations which have landed in project over last year. Materialized view, micro managed tables and workload management are some noteworthy features.
I will deep dive into some optimizations which promise to provide major performance gains. Support for ACID tables has also improved considerably. Although some of these features and enhancements are not novel but have existed for years in other DB systems, implementing them on Hive poses some unique challenges and results in lessons which are generally applicable in many other contexts. I will also provide a glimpse of what is expected to come in near future.
Speaker: Ashutosh Chauhan, Engineering Manager, Hortonworks
Getting the Most Out of Your Data in the Cloud with Cloudbreak
Cloudbreak, a part of Hortonworks Data Platform (HDP), simplifies the provisioning and cluster management within any cloud environment to help your business toward its path to a hybrid cloud architecture.
https://hortonworks.com/webinar/getting-data-cloud-cloudbreak-live-demo/
What is new in Apache Hive 3.0?
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
Apache Hadoop YARN: state of the union
The document provides an overview of the state of Apache Hadoop YARN. Key themes discussed include scaling to support very large clusters of 100,000+ nodes, improved global and fast scheduling capabilities, richer placement constraints, and enhanced support for containers, resources like GPUs and FPGAs, and services. The YARN community continues to grow with over 450 contributors.
HDF 3.1 : An Introduction to New Features
Hortonworks Data Flow 3.1 introduces new features to improve ease of use, stream processing, cross-product integration, and flow management. Key enhancements include NiFi registry for version control of flows, improved Kafka 1.0 support, and new processors for deeper ecosystem integration. HDF 3.1 provides tools for engineers to aggregate, mediate, and gain insights from data across multiple sources when deployed with Hortonworks Data Platform.
Using Spark Streaming and NiFi for the next generation of ETL in the enterprise
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Curing the Kafka blindness—Streams Messaging Manager
Companies who use Kafka today struggle with monitoring and managing Kafka clusters. Kafka is a key backbone of IoT streaming analytics applications. The challenge is understanding what is going on overall in the Kafka cluster including performance, issues and message flows. No open source tool caters to the needs of different users that work with Kafka: DevOps/developers, platform team, and security/governance teams. See how the new Hortonworks Streams Messaging Manager enables users to visualize their entire Kafka environment end-to-end and simplifies Kafka operations.
In this session learn how SMM visualizes the intricate details of how Apache Kafka functions in real time while simultaneously surfacing every nuance of tuning, optimizing, and measuring input and output. SMM will assist users to quickly understand and operate Kafka while providing the much-needed transparency that sophisticated and experienced users need to avoid all the pitfalls of running a Kafka cluster.
Fast SQL on Hadoop, Really?
The document discusses Apache Hive and Apache Druid for fast SQL on big data. It provides performance benchmarks showing Hive LLAP is faster than Presto and Spark SQL for TPC-DS queries. It describes features of Hive LLAP including in-memory caching, query result caching, and metadata caching. It also discusses new Hive 3 features like materialized views and optimizer improvements. The document then provides an overview of Apache Druid's capabilities for real-time ingestion and querying of streaming data before discussing how Hive and Druid can work together, with Hive able to push down queries to Druid.
Apache Ambari - What's New in 2.1
Ambari 2.1 includes several new features and improvements, including manual Kerberos configuration, customizable dashboards, guided configurations, rack awareness, and views framework enhancements. It adds support for Storm Nimbus HA, Ranger HA, RHEL/CentOS 7, and new JDKs. The views framework now supports auto-configuration using cluster properties and auto-creation of view instances when cluster requirements are met. Standalone Ambari servers allow running views without managing a cluster.
Hive 3 New Horizons DataWorks Summit Melbourne February 2019
Hive 3 new SQL features including LLAP, workload management, SQL over Kafka and JDBC data sources, integration with Spark via Hive Warehouse Connector, ACID 2, and constraints and default values
One Click Hadoop Clusters - Anywhere (Using Docker)
This document provides an overview of Hortonworks' one-click Hadoop clusters solution that uses Docker and Apache Ambari to provision Hadoop clusters on any infrastructure. It discusses the goals of automating and unifying the Hadoop cluster provisioning process. The technology stack uses Docker for containerization, Swarm for clustering and orchestration, Consul for service discovery and registration, and Ambari for provisioning and management. Cloudbreak is introduced as a cloud-agnostic API that abstracts Hadoop cluster provisioning using these tools, while Periscope provides heuristic scheduling and autoscaling of clusters based on service level agreements.
Managing Enterprise Hadoop Clusters with Apache Ambari
The document discusses features of the Apache Ambari platform for managing Hadoop clusters, including:
- Ambari allows provisioning, managing, and monitoring Hadoop clusters at scale through features like stacks, blueprints, views, and smart configurations.
- Stacks define Hadoop services and components and their lifecycles. Blueprints allow automated deployment of clusters. Views extend the Ambari UI.
- Other features discussed include rolling upgrades between stack versions, metrics collection and monitoring, and an alerts framework to notify users of cluster issues.
Managing Enterprise Hadoop Clusters with Apache Ambari
This document discusses Apache Ambari, an open-source platform for managing Hadoop clusters. It provides an overview of Ambari, describing its key features including stacks, blueprints, views and extensibility points. It also demonstrates Ambari's capabilities for cluster deployment, management, monitoring and upgrades through a stack and blueprint demo.
Apache Hadoop YARN: State of the Union
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Similar a Data in the Cloud Crash Course (20)
Fortifying Multi-Cluster Hybrid Cloud Data Lakes using Apache Knox
Fortifying Multi-Cluster Hybrid Cloud Data Lakes using Apache Knox
Getting the Most Out of Your Data in the Cloud with Cloudbreak
Getting the Most Out of Your Data in the Cloud with Cloudbreak
Using Spark Streaming and NiFi for the next generation of ETL in the enterprise
Using Spark Streaming and NiFi for the next generation of ETL in the enterprise
Curing the Kafka blindness—Streams Messaging Manager
Curing the Kafka blindness—Streams Messaging Manager
Hive 3 New Horizons DataWorks Summit Melbourne February 2019
Hive 3 New Horizons DataWorks Summit Melbourne February 2019
One Click Hadoop Clusters - Anywhere (Using Docker)
One Click Hadoop Clusters - Anywhere (Using Docker)
Managing Enterprise Hadoop Clusters with Apache Ambari
Managing Enterprise Hadoop Clusters with Apache Ambari
Managing Enterprise Hadoop Clusters with Apache Ambari
Managing Enterprise Hadoop Clusters with Apache Ambari
Más de DataWorks Summit
Data Science Crash Course
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache Ratis
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
Managing the Dewey Decimal System
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Practical NoSQL: Accumulo's dirlist Example
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at Uber
Danny Chen presented on Uber's use of HBase for global indexing to support large-scale data ingestion. Uber uses HBase to provide a global view of datasets ingested from Kafka and other data sources. To generate indexes, Spark jobs are used to transform data into HFiles, which are loaded into HBase tables. Given the large volumes of data, techniques like throttling HBase access and explicit serialization are used. The global indexing solution supports requirements for high throughput, strong consistency and horizontal scalability across Uber's data lake.
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
This document discusses using Apache NiFi to build a high-speed cyber security data pipeline. It outlines the challenges of ingesting, transforming, and routing large volumes of security data from various sources to stakeholders like security operations centers, data scientists, and executives. It proposes using NiFi as a centralized data gateway to ingest data from multiple sources using a single entry point, transform the data according to destination needs, and reliably deliver the data while avoiding issues like network traffic and data duplication. The document provides an example NiFi flow and discusses metrics from processing over 20 billion events through 100+ production flows and 1000+ transformations.
Supporting Apache HBase : Troubleshooting and Supportability Improvements
This document discusses supporting Apache HBase and improving troubleshooting and supportability. It introduces two Cloudera employees who work on HBase support and provides an overview of typical troubleshooting scenarios for HBase like performance degradation, process crashes, and inconsistencies. The agenda covers using existing tools like logs and metrics to troubleshoot HBase performance issues with a general approach, and introduces htop as a real-time monitoring tool for HBase.
Security Framework for Multitenant Architecture
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything Engine
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google Cloud
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Computer Vision: Coming to a Store Near You
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
Más de DataWorks Summit (20)
Floating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache Ratis
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
HBase Global Indexing to support large-scale data ingestion at Uber
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Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Supporting Apache HBase : Troubleshooting and Supportability Improvements
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Presto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything Engine
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Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
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Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
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UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Columbus Data & Analytics Wednesdays - June 2024
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HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
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Choosing The Best AWS Service For Your Website + API.pptx
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Data in the Cloud Crash Course
- 1. 1 © Hortonworks Inc. 2011–2018. All rights reserved Data in the Cloud Santhosh B Gowda Engineer, Cloudera DataWorks Summit – Melbourne Feb 2019
- 2. 2 © Hortonworks Inc. 2011–2018. All rights reserved Who Am I ? Santhosh B Gowda Engineer @ Cloudera
- 3. 3 © Hortonworks Inc. 2011–2018. All rights reserved Agenda • Introduction • Primary Use Cases • Cloudbreak Architecture • Cloudbreak Core Concepts • Credentials • Blueprints • Recipes • Images • CLI • Streaming in Cloudbreak • Shared Services Data Lake • Hands-On Lab
- 4. 4 © Hortonworks Inc. 2011–2018. All rights reserved No Upfront HW Costs $0 Unlimited Elastic Scale Ephemeral & Long-Running IT & Business Agility Why Data on Cloud ?
- 5. 5 © Hortonworks Inc. 2011–2018. All rights reserved What Is Cloudbreak ? Cloudbreak is a tool for provisioning Hadoop clusters on any cloud infrastructure Simplified Cluster Provisioning - prescriptive setup, simple automation
- 6. 6 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak: Harness the Agility of Cloud with Ease Cloudbreak • Declarative workload provisioning across multiple cloud providers • Flexible topologies and security configuration options • DevOps friendly, easy setup and simple to automate • Built-in elasticity and auto- scaling • Prescriptive integration with cloud services
- 7. 7 © Hortonworks Inc. 2011–2018. All rights reserved Dev / Test (all HDP services) BI / Analytics (Hive) Data Science (Spark) IoT Apps (Storm, HBase, Hive) Cloudbreak Cloudbreak 1. Pick a Blueprint 2. Choose a Cloud 3. Launch HDP/HDF! Example Ambari Blueprints: IoT Apps, BI / Analytics, Data Science, Dev / Test
- 8. 8 © Hortonworks Inc. 2011–2018. All rights reserved Create Cluster Manage Credentials Manage Templates Manage Network Manage Security Groups Manage Blueprints Advanced Configurati on Configure Credentials for Cloudbreak for connecting to a Cloud Provider Configure size of VM images Manage who can access HDP clusters Define network in which HDP cluster will run Define HDP Services Configure HDP Repository Define Failure Actions Cloud Provider Specific Configurations HDP Specific Configurations
- 9. 10 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak UI
- 10. 11 © Hortonworks Inc. 2011–2018. All rights reserved Manage Cluster
- 11. 12 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak CLI DevOps
- 12. 13 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak CLI
- 13. 14 © Hortonworks Inc. 2011–2018. All rights reserved “SHOW CLI COMMAND”
- 14. 15 © Hortonworks Inc. 2011–2018. All rights reserved Recipes DevOps
- 15. 16 © Hortonworks Inc. 2011–2018. All rights reserved Background: Recipes • Cloudbreak lets you provision cluster using Ambari Blueprint however not all use-cases can be addressed. • Install additional software. • System config changes. • A recipe is a script that runs on all nodes of a selected node group at a specific time. • Support for bash and python scripts. • Available hooks • Pre-ambari-start • Post-ambari-start • Post-cluster-install • Pre-termination
- 16. 17 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak: Add Recipes • Cluster Extensions > Recipes > Create • Add recipe as File, Url or Text
- 17. 18 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak: Add Recipes • Clusters > Create Cluster > Cluster Extensions
- 18. 19 © Hortonworks Inc. 2011–2018. All rights reserved Streaming in the Cloud
- 19. 20 © Hortonworks Inc. 2011–2018. All rights reserved
- 20. 21 © Hortonworks Inc. 2011–2018. All rights reserved HDF in CloudBreak 2.7 Apache NiFi and Apache NiFi Registry
- 21. 22 © Hortonworks Inc. 2011–2018. All rights reserved HDF in CloudBreak 2.7 Apache Kafka
- 22. 23 © Hortonworks Inc. 2011–2018. All rights reserved Auto Scaling
- 23. 24 © Hortonworks Inc. 2011–2018. All rights reserved Auto-Scaling • Alerts: Create metric or time-based alerts for cluster scaling • Policies: Scaling policies adjust cluster size based on activity and workload alerts • General Configurations: Boundaries and cooldown period
- 24. 25 © Hortonworks Inc. 2011–2018. All rights reserved Auto-Scaling Time-Based Alert Fire at 10:15 am everyday
- 25. 26 © Hortonworks Inc. 2011–2018. All rights reserved Auto-Scaling Metric-Based Alert Fire after NodeManagers are CRITICAL for 10 minutes
- 26. 27 © Hortonworks Inc. 2011–2018. All rights reserved Auto-Scaling Policies • Define the Scale Adjustment (Node Count, Percentage, Exact) • Select the Host Group (to Scale) • Select Alert (which when fired, executes the Policy)
- 27. 28 © Hortonworks Inc. 2011–2018. All rights reserved Data Lake Shared Services
- 28. 29 © Hortonworks Inc. 2011–2018. All rights reserved Why Data Lake Shared Services • Customers have a need to secure ephemeral workload clusters • Customers need a single metadata repository for Hive schema • Customers want a single pane of glass to define users, groups and authorization policies
- 29. 30 © Hortonworks Inc. 2011–2018. All rights reserved Introducing “Data Lake” in Cloudbreak CLOUD STORAGE WORKLOAD S Durable Ephemeral When data resides in cloud object stores (e.g. S3, ADLS, WASB, GCS), Hadoop optimizes reads/writes and acts as an intermediate cache to increase performance and decrease latency. Metastore SCHEM A Long Running Security access to workload clusters via a Protected Gateway enabled for AuthN and HTTPS. Define your data schema, security policies, and metadata catalog once for your ephemeral and always-on workloads. Atlas CATALOG Ranger POLIC Y SHARED DATA LAKE SERVICES
- 30. 31 © Hortonworks Inc. 2011–2018. All rights reserved Data Lake: Flyover LDAP/AD Hive Database Ranger Database Cloud Storage Data Lake Workload Cluster(s) Ranger Hive Metastore Hive, Spark, Zeppelin Attach
- 31. 32 © Hortonworks Inc. 2011–2018. All rights reserved Learn More • Try Cloudbreak 2.8 (TP) • http://docs.hortonworks.com
- 32. 33 © Hortonworks Inc. 2011–2018. All rights reserved Thank You
- 33. 35 © Hortonworks Inc. 2011–2018. All rights reserved Launch HDP from Cloudbreak UI (Demo#1)
- 34. 36 © Hortonworks Inc. 2011–2018. All rights reserved Launch HDF from Cloudbreak UI (Demo#2)
- 35. 37 © Hortonworks Inc. 2011–2018. All rights reserved Launch HDP from CLI (Demo#3)