Hadoop: The Unintended Benefits
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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.
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![Department of Home Affairs
• The Department of Home Affairs
• Hadoop at the Department
• Unintended Benefits?
– Solr and EDW Offload
– Graph and Data Science
Hadoop: The Unintended Benefits [UNCLASSIFIED] 2
Agenda](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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In order to share the project experience on IoT architecture and to make the project successful, we will explain the key points to be a hint from the practical experience point to avoid common common pitfalls in the IoT related project .
Data Centric Transformation in Telecom
The document discusses how telecom companies can undergo a data-centric transformation to better leverage customer data and remain competitive. It describes how telecoms are facing new challenges like social media, mobile apps, and customer expectations of better service. It argues telecoms should shift from an app-centric to data-centric model to better integrate and scale their use of data. This will allow them to gain better customer insights and optimize areas like customer experience, new digital services, and network management.
Boost Performance with Scala – Learn From Those Who’ve Done It!
Scalding is a scala DSL for Cascading. Run on Hadoop, it’s a concise, functional, and very efficient way to build big data applications. One significant benefit of Scalding is that it allows easy porting of Scalding apps from MapReduce to newer, faster execution fabrics.
In this webinar, Cyrille Chépélov, of Transparency Rights Management, will share how his organization boosted the performance of their Scalding apps by over 50% by moving away from MapReduce to Cascading 3.0 on Apache Tez. Dhruv Kumar, Hortonworks Partner Solution Engineer, will then explain how you can interact with data on HDP using Scala and leverage Scala as a programming language to develop Big Data applications.
Customer Presentation
John Villacres works in network automation and tools at Nationwide. He demonstrated Splunk to colleagues in 2012 and they now use it extensively. Splunk has improved their ability to troubleshoot issues by providing timely access to network data through custom dashboards. It has reduced resolution times for problems from days to minutes by integrating data from sources like firewalls, routers, and packet captures. More teams now use Splunk as its efficiency has allowed employees to take on new tasks while maintaining productivity.
What it takes to bring Hadoop to a production-ready state
While Hadoop may be a hot topic and is probably the buzziest big data term, the fact is that many Hadoop projects get stuck in pilot mode. We hear a number of reasons for this.
• “It’s too complicated.”
• “I don’t have the right resources.”
• “Security and compliance are never going to approve this.”
This session digs deep into why certain projects seem destined to remain in development. We’ll also cover what it takes to bring Hadoop to a production-ready state and convince management that it’s time to start using Hadoop to store and analyze real business data.
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La actualidad más candente
What s new in spark 2.3 and spark 2.4
Apache Spark 2.0 set the architectural foundations of structure in Spark, unified high-level APIs, structured streaming, and the underlying performant components like Catalyst Optimizer and Tungsten Engine. Since then the Spark community has continued to build new features and fix numerous issues in releases Spark 2.1 and 2.2.
Apache Spark 2.3 & 2.4 has made similar strides too. In this talk, we want to highlight some of the new features and enhancements, such as:
• Apache Spark and Kubernetes
• Native Vectorized ORC and SQL Cache Readers
• Pandas UDFs for PySpark
• Continuous Stream Processing
• Barrier Execution
• Avro/Image Data Source
• Higher-order Functions
Speaker: Robert Hryniewicz, AI Evangelist, Hortonworks
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
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 - 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.
Zero ETL analytics with LLAP in Azure HDInsight
It’s 2017, and big data challenges are as real as they get. Our customers have petabytes of data living in elastic and scalable commodity storage systems such as Azure Data Lake Store and Azure Blob storage.
One of the central questions today is finding insights from data in these storage systems in an interactive manner, at a fraction of the cost.
Interactive Query leverages [Hive on LLAP] in Apache Hive 2.1, brings the interactivity to your complex data warehouse style queries on large datasets stored on commodity cloud storage.
In this session, you will learn how technologies such as Low Latency Analytical Processing [LLAP] and Hive 2.x are making it possible to analyze petabytes of data with sub second latency with common file formats such as csv, json etc. without converting to columnar file formats like ORC/Parquet. We will go deep into LLAP’s performance and architecture benefits and how it compares with Spark and Presto in Azure HDInsight. We also look at how business analysts can use familiar tools such as Microsoft Excel and Power BI, and do interactive query over their data lake without moving data outside the data lake.
Speaker
Ashish Thapliyal, Principal Program Manager, Microsoft Corp
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
Benefits of an Agile Data Fabric for Business Intelligence
An agile data fabric powered by Brocade networking solutions provides benefits for business intelligence initiatives. It allows data and applications to be deployed flexibly across distributed infrastructure in a way that is automated, intelligent, and optimized for performance. Case studies demonstrate how Brocade fabrics have enabled multi-tenant data lakes and analytics platforms by integrating diverse storage, computing, and networking resources.
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
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.
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
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
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.
LLAP: Sub-Second Analytical Queries in Hive
The document discusses LLAP (Live Long and Process), a new execution engine in Apache Hive 2.0 that enables sub-second analytical queries. LLAP keeps a small subset of frequently accessed data in memory to enable faster query processing times compared to traditional Hive architectures that rely on disk access. It works by running Hive query fragments concurrently in long-running daemon processes with an in-memory cache, rather than in short-lived YARN containers. This allows queries to retrieve data from memory rather than disk, providing significant performance improvements for interactive analytics workloads. The document provides details on how LLAP is implemented and evaluates its performance benefits based on benchmarks and customer case studies.
Data in the Cloud Crash Course
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.
Interactive real-time dashboards on data streams using Kafka, Druid, and Supe...
When interacting with analytics dashboards, in order to achieve a smooth user experience, two major key requirements are quick response time and data freshness. To meet the requirements of creating fast interactive BI dashboards over streaming data, organizations often struggle with selecting a proper serving layer.
Cluster computing frameworks such as Hadoop or Spark work well for storing large volumes of data, although they are not optimized for making it available for queries in real time. Long query latencies also make these systems suboptimal choices for powering interactive dashboards and BI use cases.
This talk presents an open source real-time data analytics stack using Apache Kafka, Druid, and Superset. The stack combines the low-latency streaming and processing capabilities of Kafka with Druid, which enables immediate exploration and provides low-latency queries over the ingested data streams. Superset provides the visualization and dashboarding that integrates nicely with Druid. In this talk we will discuss why this architecture is well suited to interactive applications over streaming data, present an end-to-end demo of complete stack, discuss its key features, and discuss performance characteristics from real-world use cases. NISHANT BANGARWA, Software 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
Accelerating Big Data Insights
This document discusses Dell's solutions for big data and analytics workloads. It describes Dell's portfolio for unstructured analytics including storage, servers, and reference architectures. It also outlines Dell's vision for a unified streaming and batch analytics platform called Project Nautilus that would integrate Isilon storage with real-time stream processing.
IoT Story: From Edge to HDP
In order to share the project experience on IoT architecture and to make the project successful, we will explain the key points to be a hint from the practical experience point to avoid common common pitfalls in the IoT related project .
Data Centric Transformation in Telecom
The document discusses how telecom companies can undergo a data-centric transformation to better leverage customer data and remain competitive. It describes how telecoms are facing new challenges like social media, mobile apps, and customer expectations of better service. It argues telecoms should shift from an app-centric to data-centric model to better integrate and scale their use of data. This will allow them to gain better customer insights and optimize areas like customer experience, new digital services, and network management.
Boost Performance with Scala – Learn From Those Who’ve Done It!
Scalding is a scala DSL for Cascading. Run on Hadoop, it’s a concise, functional, and very efficient way to build big data applications. One significant benefit of Scalding is that it allows easy porting of Scalding apps from MapReduce to newer, faster execution fabrics.
In this webinar, Cyrille Chépélov, of Transparency Rights Management, will share how his organization boosted the performance of their Scalding apps by over 50% by moving away from MapReduce to Cascading 3.0 on Apache Tez. Dhruv Kumar, Hortonworks Partner Solution Engineer, will then explain how you can interact with data on HDP using Scala and leverage Scala as a programming language to develop Big Data applications.
La actualidad más candente (20)
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
Curing the Kafka Blindness – Streams Messaging Manager
Curing the Kafka Blindness – Streams Messaging Manager
Benefits of an Agile Data Fabric for Business Intelligence
Benefits of an Agile Data Fabric for Business Intelligence
Securing Data in Hybrid on-premise and Cloud Environments Using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments Using Apache Ranger
Interactive real-time dashboards on data streams using Kafka, Druid, and Supe...
Interactive real-time dashboards on data streams using Kafka, Druid, and Supe...
Boost Performance with Scala – Learn From Those Who’ve Done It!
Boost Performance with Scala – Learn From Those Who’ve Done It!
Similar a Hadoop: The Unintended Benefits
Customer Presentation
John Villacres works in network automation and tools at Nationwide. He demonstrated Splunk to colleagues in 2012 and they now use it extensively. Splunk has improved their ability to troubleshoot issues by providing timely access to network data through custom dashboards. It has reduced resolution times for problems from days to minutes by integrating data from sources like firewalls, routers, and packet captures. More teams now use Splunk as its efficiency has allowed employees to take on new tasks while maintaining productivity.
What it takes to bring Hadoop to a production-ready state
While Hadoop may be a hot topic and is probably the buzziest big data term, the fact is that many Hadoop projects get stuck in pilot mode. We hear a number of reasons for this.
• “It’s too complicated.”
• “I don’t have the right resources.”
• “Security and compliance are never going to approve this.”
This session digs deep into why certain projects seem destined to remain in development. We’ll also cover what it takes to bring Hadoop to a production-ready state and convince management that it’s time to start using Hadoop to store and analyze real business data.
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...
1) StumbleUpon uses open source tools like Kafka, HBase, Hive and Pig to build a scalable big data infrastructure to process large amounts of data from its services in real-time and batch.
2) Data is collected from various services using Kafka and stored in HBase for real-time analytics. Batch processing is done using Pig and data is loaded into Hive for ad-hoc querying.
3) The infrastructure powers various applications like recommendations, ads and business intelligence dashboards.
Taboola Road To Scale With Apache Spark
Taboola's data processing architecture has evolved over time from directly writing to databases to using Apache Spark for scalable real-time processing. Spark allows Taboola to process terabytes of data daily across multiple data centers for real-time recommendations, analytics, and algorithm calibration. Key aspects of Taboola's architecture include using Cassandra for event storage, Spark for distributed computing, Mesos for cluster management, and Zookeeper for coordination across a large Spark cluster.
Optimizing Dell PowerEdge Configurations for Hadoop
This document discusses optimizing Dell PowerEdge server configurations for Hadoop deployments. It recommends tested server configurations like the PowerEdge R720 and R720XD that provide balanced compute and storage. It also recommends a reference architecture using these servers along with networking best practices and validated software configurations from Cloudera to provide a proven, optimized big data platform.
Taking Splunk to the Next Level – Architecture
Are you outgrowing your initial Splunk deployment? Is Splunk becoming mission critical and you need to make sure it's Enterprise ready? Attend this session led by Splunk experts to learn about taking your Splunk deployment to the next level. Learn about Splunk high availability architectures with Splunk Search Head Clustering and Index Replication. Additionally, learn how to manage your deployment with Splunk’s operational and management controls to manage Splunk capacity and end user experience.
Data Science
Scalable Data Analytics, Distributed Data Analytics, Fault Tolerant Data Analytics, Big Data Analytics
Getting Started with Splunk Breakout Session
This document provides an overview and agenda for a presentation on getting started with Splunk Enterprise. The presentation covers an overview of Splunk Inc. and the Splunk platform, a live demonstration of using Splunk to install, index, search, create reports and dashboards, and set alerts. It also discusses deploying Splunk in distributed architectures, the Splunk community resources, and support options. The goal is to help attendees understand how to use the key capabilities of Splunk Enterprise.
Taking Splunk to the Next Level - Architecture Breakout Session
This document discusses strategies for scaling a Splunk deployment. It begins by describing how customers typically start with a single use case but then need to scale to handle more data and use cases. It then covers strategies for scaling the forwarding, indexing, search, and management components of Splunk. Key topics include load balancing forwarders, using indexer clustering for high availability, scaling search heads by clustering, and using the deployment server and distributed management console for centralized management. The document emphasizes planning storage capacity and I/O when scaling indexers and considering Splunk's application support when scaling search heads.
5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game Changer
Webinar by Elliott Cordo, Caserta Concepts
There is much hype and mystery surrounding Hadoop's role in analytic architecture. In this webinar, Elliott presented, in detail, the services and concepts that makes Hadoop a truly unique solution - a game changer for the enterprise. He talked about the real benefits of a distributed file system, the multi workload processing capabilities enabled by YARN, and the 3 other important things you need to know about Hadoop.
To access the recorded webinar, visit the event site: https://www.brighttalk.com/webcast/9061/131029
For more information the services and solutions that Caserta Concepts offers, please visit http://casertaconcepts.com/
Self-Service BI for big data applications using Apache Drill (Big Data Amster...
Modern big data applications such as social, mobile, web and IoT deal with a larger number of users and larger amount of data than the traditional transactional applications. The datasets associated with these applications evolve rapidly, are often self-describing and can include complex types such as JSON and Parquet. In this demo we will show how Apache Drill can be used to provide low latency queries natively on rapidly evolving multi-structured datasets at scale.
Self-Service BI for big data applications using Apache Drill (Big Data Amster...
Modern big data applications such as social, mobile, web and IoT deal with a larger number of users and larger amount of data than the traditional transactional applications. The datasets associated with these applications evolve rapidly, are often self-describing and can include complex types such as JSON and Parquet. In this demo we will show how Apache Drill can be used to provide low latency queries natively on rapidly evolving multi-structured datasets at scale.
Fast, Flexible Application Development with Oracle Database Cloud Service
Developing applications to run on the most important Database Manager in the world ? Why not do it in the cloud? With Oracle Database Cloud Service, developers can quickly and easily access the power and flexibility of the Oracle database in the cloud. With a choice between an instance or a dedicated database with full administrative control, or a schema dedicated to a development platform and full deployment managed by Oracle, developers can decide how much control they have over their development environments. Attend this session to learn more about the features and benefits of Oracle Database Cloud.
Denver Big Data Analytics Day
Many companies have discovered that there is “gold” in their server log files and machine data. Closely monitoring this data can improve security, help prevent costly outages and reduce the time it takes to recover from a problem. In this presentation, GTRI’s Micah Montgomery explains how operational intelligence can be gained from machine data, and how Splunk Enterprise can turn this data into actionable insights. Also presenting was NetApp’s Steve Fritzinger, who discussed how to manage the challenges of capturing and storing a flood of data without breaking the bank.
Presented at "Denver Big Data Analytics Day" on May 18, 2016 at GTRI.
Splunk hunkbeta
Splunk Announces Beta Version of Hunk: Splunk Analytics for Hadoop
New Software Product to Explore, Analyze and Visualize Data in Hadoop
HADOOP SUMMIT NORTH AMERICA 2013, SAN JOSE – June 26, 2013 - Splunk Inc. (NASDAQ: SPLK), the leading software platform for real-time operational intelligence, today announced the beta version of Hunk: Splunk® Analytics for Hadoop. Hunk (beta) is a new software product from Splunk that integrates exploration, analysis and visualization of data in Hadoop. Building upon Splunk’s years of experience with big data analytics technology deployed at thousands of customers, Hunk drives dramatic improvements in the speed and simplicity of interacting with and analyzing data in Hadoop without programming, costly integrations or forced data migrations. Watch the Hunk video to learn more.
Using Scalding for Data Driven Product Development at LinkedIn
This document discusses using Scalding, which combines Scala and Hadoop, for data-driven product development. Scalding provides a domain-specific language for writing MapReduce jobs in Scala that allows processing large datasets in Hadoop. The document describes how LinkedIn uses Scalding for various tasks including processing web and application data at large scales. It highlights benefits like succinct code, abstraction, and running thousands of Scalding jobs successfully in LinkedIn's production environment.
Silicon Valley Code Camp 2016 - MongoDB in production
What to look for when deploying MongoDB in Production, and a list of the most common issues reported by customers to the support team
Ncku csie talk about Spark
This document provides an introduction and agenda for a presentation on Spark. It discusses how Spark is a fast engine for large-scale data processing and how it improves on MapReduce. Spark stores data in memory across clusters to allow for faster iterative computations versus writing to disk with MapReduce. The presentation will demonstrate Spark concepts through word count and log analysis examples and provide an overview of Spark's Resilient Distributed Datasets (RDDs) and directed acyclic graph (DAG) execution model.
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production
What’s important about a technology is what you can use it to do. I’ve looked at what a number of groups are doing with Apache Hadoop and NoSQL in production, and I will relay what worked well for them and what did not. Drawing from real world use cases, I show how people who understand these new approaches can employ them well in conjunction with traditional approaches and existing applications. Thread Detection, Datawarehouse optimization, Marketing Efficiency, Biometric Database are some examples exposed during this presentation.
Gab Genai Cloudera - Going Beyond Traditional Analytic
This document discusses Intel and Cloudera's partnership in helping organizations leverage big data analytics. It provides an overview of Cloudera's history and capabilities in supporting enterprises with Hadoop-based solutions. It then contrasts traditional analytics approaches that brought data to compute with Cloudera's approach of bringing compute to data using their Enterprise Data Hub. Several case studies are presented of organizations achieving new insights and business value through Cloudera's platform. The document emphasizes that Cloudera offers an open, scalable and cost-effective platform for various analytics workloads and enables a thriving ecosystem of partners.
Similar a Hadoop: The Unintended Benefits (20)
What it takes to bring Hadoop to a production-ready state
What it takes to bring Hadoop to a production-ready state
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...
Optimizing Dell PowerEdge Configurations for Hadoop
Optimizing Dell PowerEdge Configurations for Hadoop
Taking Splunk to the Next Level - Architecture Breakout Session
Taking Splunk to the Next Level - Architecture Breakout Session
Self-Service BI for big data applications using Apache Drill (Big Data Amster...
Self-Service BI for big data applications using Apache Drill (Big Data Amster...
Self-Service BI for big data applications using Apache Drill (Big Data Amster...
Self-Service BI for big data applications using Apache Drill (Big Data Amster...
Fast, Flexible Application Development with Oracle Database Cloud Service
Fast, Flexible Application Development with Oracle Database Cloud Service
Using Scalding for Data Driven Product Development at LinkedIn
Using Scalding for Data Driven Product Development at LinkedIn
Silicon Valley Code Camp 2016 - MongoDB in production
Silicon Valley Code Camp 2016 - MongoDB in production
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production
Gab Genai Cloudera - Going Beyond Traditional Analytic
Gab Genai Cloudera - Going Beyond Traditional Analytic
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.
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Generating privacy-protected synthetic data using Secludy and Milvus
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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...
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。
Main news related to the CCS TSI 2023 (2023/1695)
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
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Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
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Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
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Taking AI to the Next Level in Manufacturing.pdf
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June Patch Tuesday
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Presentation of the OECD Artificial Intelligence Review of Germany
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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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Hadoop: The Unintended Benefits
- 1. Department of Home Affairs Hadoop: The Unintended Benefits Steven O’Neill, Director EDW Platforms Dwane Hall, Hadoop Developer February 2019
- 2. Department of Home Affairs • The Department of Home Affairs • Hadoop at the Department • Unintended Benefits? – Solr and EDW Offload – Graph and Data Science Hadoop: The Unintended Benefits [UNCLASSIFIED] 2 Agenda
- 3. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 3 The Department of Home Affairs
- 4. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 4 The Department of Home Affairs Overview 2017-18
- 5. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 5
- 6. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 6 Passenger Systems, Teradata and Hadoop
- 7. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 7 Early Learnings • Keep it simple • Run your own race • Use your existing standards • Vendor engagement • Expect rework
- 8. Department of Home Affairs • Initial use-cases are working • Once you have done the hard work to get going, the hard work really starts: – Use cases become more complex – Number of tools increases – Expectations build • Expectations. There’s a conflict between: – The possible – The in-progress – The in-Production Hadoop: The Unintended Benefits [UNCLASSIFIED] 8 Next Steps … and the unintended benefits of using an open, multi-purpose platform
- 9. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 9 Solr: Search Requirements Search a lot of free text rows quickly • Billions of rows • Business require all of history (i.e. no date filters or partitions) • ‘Like’/wildcard searching required Current solution /problems • Build integrated data on Teradata EDW • Perform full table searches using %like% clause • Can be slow when searching (i.e. >4 billion row tables, most tables >100 million rows) Examples: • Select * from Contact where Name like ‘%Jonesy@skiclub.com%’; • Select * from Imports where Goods like ‘%snowboard%’;
- 10. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 10 Solr – A Quick Overview ID Description 123 Garmin Forerunner 456 Snowboard bindings 789 Hadoop for Dummies 888 Snowboard 999 Garmin Description ID Garmin 123, 999 Forerunner 123, 143 Snowboard 456, 888 Bindings 789, 1001 Hadoop 789, 5421, 652 Select * from Imports where Description like ‘%snowboard%’; http://hadoop:3000/solr/imports/select? q=description:snowboard - Full Table Scan (ALL AMPS) - Like Wildcard search (can’t use secondary index) - If table is large (billions of rows) then process can be slow if no date partition (PPI) is specified - Inverted Index lookup (fast) - Ideally you will end up with less terms than documents - Additional functionality (soundex / phonetic / faceting / autocomplete) comes with SOLR / Lucene RDMS: SOLR:
- 11. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 11 Solr with Hadoop
- 12. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 12 Solr + Data Service TERADATA Data Services INFINIBAND / BYNET D E L T A INTEGRATE 1. SOLR search (return ID) 2. Teradata lookup (return detail)ACQUIRE Web App
- 13. Department of Home Affairs • …Solr has no LDAP authentication out of the box • Dynamic and not temporal index How we do it? • Multi-layered • Kerberos and Ranger for indexes in HDFS (Not Kerberos Authentication Plugin) • SSL • Solr Basic Authentication and Rule-Based Authorisation Plugin Hadoop: The Unintended Benefits [UNCLASSIFIED] 13 Security - Relational to Unstructured
- 14. Department of Home Affairs • Collection based – document level in the future • All result payloads are logged and stored with a UUID • Data Services for user and audit logging - Traceability Hadoop: The Unintended Benefits [UNCLASSIFIED] 14 Security Detail
- 15. Department of Home Affairs • Extensive ecosystem which can be overwhelming. Start simple. • DEV environment representative of HLE’s (Multi-Node, Security) • Plan for entire index rebuilds • Use collection aliases, request params api • Manage expectations - Solr is not a database • Index definition affects performance. Test at scale (Jmeter, SolrMeter) • Rework python script (HDF Kafka + NiFi) • Devops for deployment • Autoscaling, document level security Hadoop: The Unintended Benefits [UNCLASSIFIED] 15 Lessons Learned + Next Steps
- 16. Department of Home Affairs • Identifying networks of persons of interest • Predictive analytics using demographics • Automation of threat discovery • Process complex relationships • Identify soft links between entities • Interactive tooling for analysts • For use by: – Data scientists – Analysts Hadoop: The Unintended Benefits [UNCLASSIFIED] 16 JanusGraph Use Cases
- 17. Department of Home Affairs • Scalable: Optimised for storing billions of vertices and edges • Works with the Hadoop ecosystem • Can perform fuzzy searching and graph traversal • Fast: Sub-second query performance response times • Supports unstructured, semi-structured and structured data • Supported by a variety of visualisation tools • Backed by Hortonworks and Google • Fit with our “Open Source First” principle Hadoop: The Unintended Benefits [UNCLASSIFIED] 17 JanusGraph Why JanusGraph?
- 18. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 18 JanusGraph Architecture
- 19. Department of Home Affairs Method Results Records per Sec Single-threaded batch process load into HBase via JanusGraph Poor Low ‘000s Parallelised load into HBase via JanusGraph Good 10k+ per mapper Bulk Spark OLAP load into HBase via JanusGraph Very Good 10k+ Parallelised load into Solr through the python Teradata Script Operator Good 10k+ NiFi streaming load process into HBase/Solr Excellent 10k+ constant stream Hadoop: The Unintended Benefits [UNCLASSIFIED] 19 JanusGraph Load Techniques and Performance To keep the graph clean, the existence of vertices and edges were checked when loading
- 20. Department of Home Affairs • Authentication – The authentication of Hadoop services are secured using Kerberos. • Authorisation – Authorisation of data and services are governed through: – Access Control Lists – Ranger policies • Encryption – TLS encryption is enabled to enforce secure communication channels. – Encryption of data at rest enforced at the appliance level. • Firewall – IP table rules applied across the cluster. Hadoop: The Unintended Benefits [UNCLASSIFIED] 20 JanusGraph Security Hardening
- 21. Department of Home Affairs • Visualisation tools: – R/Shiny for data science – Linkurious for analysts • Graph analytics: – Timeline – Path – Connectivity – Community – Centrality – PageRank Hadoop: The Unintended Benefits [UNCLASSIFIED] 21 JanusGraph Visualisation Solr search JanusGraph traversal
- 22. Department of Home Affairs • Collaboration between Data Engineering and Data Science is invaluable • Start with a generic design • Load data using simple batch methods before using advanced streaming methods • Open source incubator projects can be buggy • Issues were encountered when using a JanusGraph mixed index against a secured Solr environment • JanusGraph is still not part of the core Hadoop distribution and stack • Start in a discovery environment, before proceeding to a 24/7 supported production environment Hadoop: The Unintended Benefits [UNCLASSIFIED] 22 JanusGraph Lesson learned
- 23. Department of Home Affairs Hadoop: The Unintended Benefits [UNCLASSIFIED] 23 Questions?