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Big data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Metadata in upstream sources can ‘drift’ due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail. StreamSets Data Collector (SDC) is an Apache 2.0 licensed open source platform for building big data ingest pipelines that allows you to design, execute and monitor robust data flows. In this session we’ll look at how SDC’s “intent-driven” approach keeps the data flowing, with a particular focus on clustered deployment with Spark and other exciting Spark integrations in the works.
Building Data Pipelines with Spark and StreamSets
Building Data Pipelines with Spark and StreamSets
Pat Patterson
Enterprises are now faced with wrangling massive volumes of complex, streaming data from a variety of different sources, a new paradigm known as extreme data. However, the traditional data integration model that’s based on structured batch data and stable data movement patterns makes it difficult to analyze extreme data in real-time. Join Matt Hawkins, Principal Solutions Architect at Kinetica and Mark Brooks, Solution Engineer at StreamSets as they share how innovative organizations are modernizing their data stacks with StreamSets and Kinetica to enable faster data movement and analysis.In this webinar we’ll explore: The modern data architecture required for dealing with extreme data How StreamSets enables continuous data movement and transformation across the enterprise How Kinetica harnesses the power of GPUs to accelerate analytics on streaming data A live demo of StreamSets and Kinetica connector to enable high speed data ingestion, queries and data visualization
Webinar: The Modern Streaming Data Stack with Kinetica & StreamSets
Webinar: The Modern Streaming Data Stack with Kinetica & StreamSets
Kinetica
Slides from my talk at the Hadoop User Group Ireland meetup on June 13th 2016: building a data pipeline to ingest data from sources of different nature into Hadoop in minutes (and no coding at all) using the Open Source Streamsets Data Collector tool.
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Guglielmo Iozzia
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh? In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry. The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems. This session is targeted for architects, decision-makers, data-engineers, and system designers.
Architect’s Open-Source Guide for a Data Mesh Architecture
Architect’s Open-Source Guide for a Data Mesh Architecture
Databricks
Building Data Pipelines with Spark and StreamSets
Spark Summit EU talk by Pat Patterson
Spark Summit EU talk by Pat Patterson
Spark Summit
Apache Druid is a high-performance distributed analytics store for modern analytics applications. It supports ingesting millions of events per second and sub-second query processing. Druid supports various types of data sources for ingestion, including Apache Kafka. You can immediately query on stream events once they get ingested into Druid. Since Kafka provides scalable and robust data delivery while Druid supports advanced complex analysis on streams, Kafka and Druid are widely used together for BI and operational analytics use cases, which require interactivity, scalability, real-time, and performance. This talk is based on our real-world experiences building out streaming analytics stacks powering production use cases across many industries.
Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...
Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...
HostedbyConfluent
We’re always told to ‘Go for the Gold!,’ but how do we get there? This talk will walk you through the process of moving your data to the finish fine to get that gold metal! A common data engineering pipeline architecture uses tables that correspond to different quality levels, progressively adding structure to the data: data ingestion (‘Bronze’ tables), transformation/feature engineering (‘Silver’ tables), and machine learning training or prediction (‘Gold’ tables). Combined, we refer to these tables as a ‘multi-hop’ architecture. It allows data engineers to build a pipeline that begins with raw data as a ‘single source of truth’ from which everything flows. In this session, we will show how to build a scalable data engineering data pipeline using Delta Lake, so you can be the champion in your organization.
Simplify and Scale Data Engineering Pipelines with Delta Lake
Simplify and Scale Data Engineering Pipelines with Delta Lake
Databricks
While we frequently talk about how to build interesting products on top of machine and event data, the reality is that collecting, organizing, providing access to, and managing this data is where most people get stuck. In this session, we’ll follow the flow of data through an end to end system built to handle tens of terabytes per day of event-oriented data, providing real time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive are actually stitched together; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality. This session is especially recommended for data infrastructure engineers and architects planning, building, or maintaining similar systems.
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Building an Event-oriented...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Building an Event-oriented...
Data Con LA
Recomendados
Big data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Metadata in upstream sources can ‘drift’ due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail. StreamSets Data Collector (SDC) is an Apache 2.0 licensed open source platform for building big data ingest pipelines that allows you to design, execute and monitor robust data flows. In this session we’ll look at how SDC’s “intent-driven” approach keeps the data flowing, with a particular focus on clustered deployment with Spark and other exciting Spark integrations in the works.
Building Data Pipelines with Spark and StreamSets
Building Data Pipelines with Spark and StreamSets
Pat Patterson
Enterprises are now faced with wrangling massive volumes of complex, streaming data from a variety of different sources, a new paradigm known as extreme data. However, the traditional data integration model that’s based on structured batch data and stable data movement patterns makes it difficult to analyze extreme data in real-time. Join Matt Hawkins, Principal Solutions Architect at Kinetica and Mark Brooks, Solution Engineer at StreamSets as they share how innovative organizations are modernizing their data stacks with StreamSets and Kinetica to enable faster data movement and analysis.In this webinar we’ll explore: The modern data architecture required for dealing with extreme data How StreamSets enables continuous data movement and transformation across the enterprise How Kinetica harnesses the power of GPUs to accelerate analytics on streaming data A live demo of StreamSets and Kinetica connector to enable high speed data ingestion, queries and data visualization
Webinar: The Modern Streaming Data Stack with Kinetica & StreamSets
Webinar: The Modern Streaming Data Stack with Kinetica & StreamSets
Kinetica
Slides from my talk at the Hadoop User Group Ireland meetup on June 13th 2016: building a data pipeline to ingest data from sources of different nature into Hadoop in minutes (and no coding at all) using the Open Source Streamsets Data Collector tool.
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Guglielmo Iozzia
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh? In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry. The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems. This session is targeted for architects, decision-makers, data-engineers, and system designers.
Architect’s Open-Source Guide for a Data Mesh Architecture
Architect’s Open-Source Guide for a Data Mesh Architecture
Databricks
Building Data Pipelines with Spark and StreamSets
Spark Summit EU talk by Pat Patterson
Spark Summit EU talk by Pat Patterson
Spark Summit
Apache Druid is a high-performance distributed analytics store for modern analytics applications. It supports ingesting millions of events per second and sub-second query processing. Druid supports various types of data sources for ingestion, including Apache Kafka. You can immediately query on stream events once they get ingested into Druid. Since Kafka provides scalable and robust data delivery while Druid supports advanced complex analysis on streams, Kafka and Druid are widely used together for BI and operational analytics use cases, which require interactivity, scalability, real-time, and performance. This talk is based on our real-world experiences building out streaming analytics stacks powering production use cases across many industries.
Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...
Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...
HostedbyConfluent
We’re always told to ‘Go for the Gold!,’ but how do we get there? This talk will walk you through the process of moving your data to the finish fine to get that gold metal! A common data engineering pipeline architecture uses tables that correspond to different quality levels, progressively adding structure to the data: data ingestion (‘Bronze’ tables), transformation/feature engineering (‘Silver’ tables), and machine learning training or prediction (‘Gold’ tables). Combined, we refer to these tables as a ‘multi-hop’ architecture. It allows data engineers to build a pipeline that begins with raw data as a ‘single source of truth’ from which everything flows. In this session, we will show how to build a scalable data engineering data pipeline using Delta Lake, so you can be the champion in your organization.
Simplify and Scale Data Engineering Pipelines with Delta Lake
Simplify and Scale Data Engineering Pipelines with Delta Lake
Databricks
While we frequently talk about how to build interesting products on top of machine and event data, the reality is that collecting, organizing, providing access to, and managing this data is where most people get stuck. In this session, we’ll follow the flow of data through an end to end system built to handle tens of terabytes per day of event-oriented data, providing real time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive are actually stitched together; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality. This session is especially recommended for data infrastructure engineers and architects planning, building, or maintaining similar systems.
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Building an Event-oriented...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Building an Event-oriented...
Data Con LA
Big data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Upstream data sources can 'drift' due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail. StreamSets Data Collector (SDC) is an open source platform for building big data ingest pipelines that allows you to design, execute and monitor robust data flows. In this session we'll look at how SDC's "intent-driven" approach keeps the data flowing, whether you're processing data 'off-cluster', in Spark, or in MapReduce. StreamSets software delivers performance management for data flows that feed the next generation of big data applications. Its mission is to bring operational excellence to the management of data in motion, so that data arrives on time and with quality, accelerating analysis and decision making. StreamSets Data Collector is in use at hundreds of companies where it brings unprecedented visibility into and control over data as it moves between an expanding variety of sources and destinations. Speakers: Pat Patterson has been working with Internet technologies since 1997, building software and working with communities at Sun Microsystems, Huawei, Salesforce and StreamSets. At Sun, Pat was the community lead for the OpenSSO open source project, while at Huawei he developed cloud storage infrastructure software. Part of the developer evangelism team at Salesforce, Pat focused on identity, integration and the Internet of Things. Now community champion at StreamSets, Pat is responsible for the care and feeding of the StreamSets open source community.
August 2016 HUG: Open Source Big Data Ingest with StreamSets Data Collector
August 2016 HUG: Open Source Big Data Ingest with StreamSets Data Collector
Yahoo Developer Network
Netflix processes trillions of events and petabytes of data a day in the Keystone data pipeline, which is built on top of Apache Flink. As Netflix has scaled up original productions annually enjoyed by more than 150 million global members, data integration across the streaming service and the studio has become a priority. Scalably integrating data across hundreds of different data stores in a way that enables us to holistically optimize cost, performance and operational concerns presented a significant challenge. Learn how we expanded the scope of the Keystone pipeline into the Netflix Data Mesh, our real-time, general-purpose, data transportation platform for moving data between Netflix systems. The Keystone Platform’s unique approach to declarative configuration and schema evolution, as well as our approach to unifying batch and streaming data and processing will be covered in depth.
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Flink Forward
As presented: Sajith Appukuttan, Solution Architect, Databricks Sept 12, 2019 at Vancouver Spark Meetup Abstract: Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
Delta Lake: Open Source Reliability w/ Apache Spark
Delta Lake: Open Source Reliability w/ Apache Spark
George Chow
Comcast is one of the leading providers of communications, entertainment, and cable products and services. At the heart of it is Comcast RDK providing the backbone of telemetry to the industry. RDK (Reference Design Kit) is pre-bundled opensource firmware for a complete home platform covering video, broadband and IoT devices. RDK team at Comcast analyzes petabytes of data, collected every 15 minutes from 70 million devices (video and broadband and IoT devices) installed in customer homes. They run ETL and aggregation pipelines and publish analytical dashboards on a daily basis to reduce customer calls and firmware rollout. The analysis is also used to calculate WIFI happiness index which is a critical KPI for Comcast customer experience. In addition to this, RDK team also does release tracking by analyzing the RDK firmware quality. SQL Analytics allows customers to operate a lakehouse architecture that provides data warehousing performance at data lake economics for up to 4x better price/performance for SQL workloads than traditional cloud data warehouses. We present the results of the “Test and Learn” with SQL Analytics and the delta engine that we worked in partnership with the Databricks team. We present a quick demo introducing the SQL native interface, the challenges we faced with migration, The results of the execution and our journey of productionizing this at scale.
SQL Analytics Powering Telemetry Analysis at Comcast
SQL Analytics Powering Telemetry Analysis at Comcast
Databricks
"Comcast has made a concerted effort to transform itself from a cable/ISP company to a technology company. Data-driven decision making is at the heart of this transformation, and we use data to understand how customers interact with our products, and we see data as the most truthful representation of the voice of our customer. My team, Product Analytics & behavior science (PABS) team plays the role as interpreter, transforming data into consumable insights. The X1 entertainment operating system, is one of the largest video streaming platforms in the world, and our customers consume more than a billion hours of content a week on X1. Our team consumes X1 telemetry at a rate of more than 25TBs of data per day and uses this data to inform our product teams members about the performance of and engagement with the platform. We also use this data to research customer behaviors to help better inform our product team members about areas of opportunity in our products, which range from fixing bugs to creating new features. To power these insights, we need to have a reliable real-time data pipelines to deliver these insights, and we need our data scientists and data engineers to be able to quickly and efficiently be able to develop and commit new code to ensure we can measure new features the product teams are developing. To do this in an environment at this scale, we have been using Databricks, and Databricks delta to gain operational efficiencies, optimization and cost savings. Some of the features from delta that we took advantage of to achieve the desired levels of efficiencies, optimization and cost savings are: · Distributed writes to s3 (essentially eliminating 500 errors) · s3 log with fast reads and ACID transactions (massive increases in s3 scans/reads, and enabling consistent views of the bucket/table) · Vacuum · Pptimize (which has allowed us to reduce a 640 node job to 40, and massively increase efficiencies of our clusters as well as our DS/DE’s)"
Building Sessionization Pipeline at Scale with Databricks Delta
Building Sessionization Pipeline at Scale with Databricks Delta
Databricks
"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo. Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow). Key Takeaways: How Databricks & Snowflake work; Why they're so powerful; How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"
Databricks + Snowflake: Catalyzing Data and AI Initiatives
Databricks + Snowflake: Catalyzing Data and AI Initiatives
Databricks
Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a “pure SaaS” company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems. As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark. In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism’s Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support interactive user experience. Finally, we will share the future challenges for 2018 and beyond.
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...
Databricks
Learn more about Redash from our summit keynote during this product walkthrough. Redash makes it easy to explore, query, visualize, and share data.
Redash: Open Source SQL Analytics on Data Lakes
Redash: Open Source SQL Analytics on Data Lakes
Databricks
In this talk, we’ll compare different data privacy techniques & protection of personally identifiable information and their effects on statistical usefulness, re-identification risks, data schema, format preservation, read & write performance. We’ll cover different offense and defense techniques. You’ll learn what k-anonymity and quasi-identifier are. Think of discovering the world of suppression, perturbation, obfuscation, encryption, tokenization, watermarking with elementary code examples, in case no third-party products cannot be used. We’ll see what approaches might be adopted to minimize the risks of data exfiltration.
Data Privacy with Apache Spark: Defensive and Offensive Approaches
Data Privacy with Apache Spark: Defensive and Offensive Approaches
Databricks
Spark is in-memory, Redis is in-memery. The Spark-Redis connector gives Spark access to Redis' data structures as RDDs. Redis, with its blazing fast performance and optimized in-memory data structures, reduces Spark processing time by up to 98%. In this talk, Dave will share the top use cases for Spark-Redis such as time-series, recommendations and real-time bid management.
Big Data Day LA 2016/ NoSQL track - Analytics at the Speed of Light with Redi...
Big Data Day LA 2016/ NoSQL track - Analytics at the Speed of Light with Redi...
Data Con LA
In Spark 2.0, we introduced Structured Streaming, which allows users to continually and incrementally update your view of the world as new data arrives, while still using the same familiar Spark SQL abstractions. I talk about progress we’ve made since then on robustness, latency, expressiveness and observability, using examples of production end-to-end continuous applications.
Insights Without Tradeoffs Using Structured Streaming keynote by Michael Armb...
Insights Without Tradeoffs Using Structured Streaming keynote by Michael Armb...
Spark Summit
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.
Intro to databricks delta lake
Intro to databricks delta lake
Mykola Zerniuk
Columbia is a data-driven enterprise, integrating data from all line-of-business-systems to manage its wholesale and retail businesses. This includes integrating real-time and batch data to better manage purchase orders and generate accurate consumer demand forecasts.
Columbia Migrates from Legacy Data Warehouse to an Open Data Platform with De...
Columbia Migrates from Legacy Data Warehouse to an Open Data Platform with De...
Databricks
Organizations are adopting data digitization and data-driven decision making is at the heart of this transformation. Cloud Data Lakes and Datawarehouses provide great flexibility to proto-type and roll out applications continuously at much lower costs. Transactional databases are optimized for processing huge volumes of transactions in real-time, whereas the cloud data lake needs to be optimized for analyzing huge volumes of data quickly. This brings about a challenge in creating a streamlined data flow process from capturing realtime transactions into a cloud datawarehouse to drive realtime insights in a scalable and cost effective manner. In this session, we’ll show how organizations can easily overcome that challenge by adopting a robust platform with StreamSets and Delta Lake. StreamSets provides a no-code framework to automate ingestion of transactional data and data processing on Spark, while Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
Power Your Delta Lake with Streaming Transactional Changes
Power Your Delta Lake with Streaming Transactional Changes
Databricks
This ebook deep dives into Apache Spark optimizations that improve performance, reduce costs and deliver unmatched scale https://www.qubole.com/resources/ebooks/accelerating-time-to-value-of-big-data-of-apache-spark
Ebooks - Accelerating Time to Value of Big Data of Apache Spark | Qubole
Ebooks - Accelerating Time to Value of Big Data of Apache Spark | Qubole
Vasu S
Presentation on using Spark for Structure Streaming of data from Apache Kafka or Event Hubs for Kafka.
Spark Streaming with Azure Databricks
Spark Streaming with Azure Databricks
Dustin Vannoy
<p>In this talk, we will highlight major efforts happening in the Spark ecosystem. In particular, we will dive into the details of adaptive and static query optimizations in Spark 3.0 to make Spark easier to use and faster to run. We will also demonstrate how new features in Koalas, an open source library that provides Pandas-like API on top of Spark, helps data scientists gain insights from their data quicker.</p>
New Developments in the Open Source Ecosystem: Apache Spark 3.0, Delta Lake, ...
New Developments in the Open Source Ecosystem: Apache Spark 3.0, Delta Lake, ...
Databricks
We present our solution for building an AI Architecture that provides engineering teams the ability to leverage data to drive insight and help our customers solve their problems. We started with siloed data, entities that were described differently by each product, different formats, complicated security and access schemes, data spread over numerous locations and systems.
Democratizing Data
Democratizing Data
Databricks
At Abnormal Security, Spark has played a fundamental role in helping us create an ML system that detects thousands of sophisticated email threats every day. Initially, we set up our Spark infrastructure using YARN on EMR because we had previous experience with it. But after growing very quickly in a short amount of time, we found ourselves spending too much time solving problems with our Spark infrastructure and less time solving problems for our customers. Given we’re in a high growth environment where the only constant is change, we asked ourselves: aren’t these problems only going to get worse as we add more employees, more products, and more data? Over the past few months, Abnormal Security executed a full migration of our Spark infrastructure to Databricks, not only improving cost, operational overhead, and developer productivity, but simultaneously laying the foundation for a modern Data Platform via the Lakehouse architecture. In this talk, we’ll cover how we executed the migration in a few months’ time, from pre-Databricks-POC, through the POC, through the migration itself. We’ll talk about how to really figure out exactly what it is that you care about when evaluating Databricks, splitting out the must-haves from the nice-to-haves, so that you can best utilize the Databricks trial and have concrete and measurable results. We’ll talk about the work we did to execute the migration and the tooling we created to minimize downtime and properly measure performance and cost. Finally, we’ll talk about how the move to Databricks not only solved problems with our legacy Spark infrastructure, but will save us huge amounts of time in the long-run from adopting a Lakehouse architecture.
Migrating Your Data Platform At a High Growth Startup
Migrating Your Data Platform At a High Growth Startup
Databricks
Intuit Analytics Cloud 101
Intuit Analytics Cloud 101
Intuit Analytics Cloud 101
DataWorks Summit/Hadoop Summit
Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Enterprise Knowledge
Explore the leading Large Language Models (LLMs) and their capabilities with a comprehensive evaluation. Dive into their performance, architecture, and applications to gain insights into the state-of-the-art in natural language processing. Discover which LLM best suits your needs and stay ahead in the world of AI-driven language understanding.
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
ChristopherTHyatt
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Big data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Upstream data sources can 'drift' due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail. StreamSets Data Collector (SDC) is an open source platform for building big data ingest pipelines that allows you to design, execute and monitor robust data flows. In this session we'll look at how SDC's "intent-driven" approach keeps the data flowing, whether you're processing data 'off-cluster', in Spark, or in MapReduce. StreamSets software delivers performance management for data flows that feed the next generation of big data applications. Its mission is to bring operational excellence to the management of data in motion, so that data arrives on time and with quality, accelerating analysis and decision making. StreamSets Data Collector is in use at hundreds of companies where it brings unprecedented visibility into and control over data as it moves between an expanding variety of sources and destinations. Speakers: Pat Patterson has been working with Internet technologies since 1997, building software and working with communities at Sun Microsystems, Huawei, Salesforce and StreamSets. At Sun, Pat was the community lead for the OpenSSO open source project, while at Huawei he developed cloud storage infrastructure software. Part of the developer evangelism team at Salesforce, Pat focused on identity, integration and the Internet of Things. Now community champion at StreamSets, Pat is responsible for the care and feeding of the StreamSets open source community.
August 2016 HUG: Open Source Big Data Ingest with StreamSets Data Collector
August 2016 HUG: Open Source Big Data Ingest with StreamSets Data Collector
Yahoo Developer Network
Netflix processes trillions of events and petabytes of data a day in the Keystone data pipeline, which is built on top of Apache Flink. As Netflix has scaled up original productions annually enjoyed by more than 150 million global members, data integration across the streaming service and the studio has become a priority. Scalably integrating data across hundreds of different data stores in a way that enables us to holistically optimize cost, performance and operational concerns presented a significant challenge. Learn how we expanded the scope of the Keystone pipeline into the Netflix Data Mesh, our real-time, general-purpose, data transportation platform for moving data between Netflix systems. The Keystone Platform’s unique approach to declarative configuration and schema evolution, as well as our approach to unifying batch and streaming data and processing will be covered in depth.
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Flink Forward
As presented: Sajith Appukuttan, Solution Architect, Databricks Sept 12, 2019 at Vancouver Spark Meetup Abstract: Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
Delta Lake: Open Source Reliability w/ Apache Spark
Delta Lake: Open Source Reliability w/ Apache Spark
George Chow
Comcast is one of the leading providers of communications, entertainment, and cable products and services. At the heart of it is Comcast RDK providing the backbone of telemetry to the industry. RDK (Reference Design Kit) is pre-bundled opensource firmware for a complete home platform covering video, broadband and IoT devices. RDK team at Comcast analyzes petabytes of data, collected every 15 minutes from 70 million devices (video and broadband and IoT devices) installed in customer homes. They run ETL and aggregation pipelines and publish analytical dashboards on a daily basis to reduce customer calls and firmware rollout. The analysis is also used to calculate WIFI happiness index which is a critical KPI for Comcast customer experience. In addition to this, RDK team also does release tracking by analyzing the RDK firmware quality. SQL Analytics allows customers to operate a lakehouse architecture that provides data warehousing performance at data lake economics for up to 4x better price/performance for SQL workloads than traditional cloud data warehouses. We present the results of the “Test and Learn” with SQL Analytics and the delta engine that we worked in partnership with the Databricks team. We present a quick demo introducing the SQL native interface, the challenges we faced with migration, The results of the execution and our journey of productionizing this at scale.
SQL Analytics Powering Telemetry Analysis at Comcast
SQL Analytics Powering Telemetry Analysis at Comcast
Databricks
"Comcast has made a concerted effort to transform itself from a cable/ISP company to a technology company. Data-driven decision making is at the heart of this transformation, and we use data to understand how customers interact with our products, and we see data as the most truthful representation of the voice of our customer. My team, Product Analytics & behavior science (PABS) team plays the role as interpreter, transforming data into consumable insights. The X1 entertainment operating system, is one of the largest video streaming platforms in the world, and our customers consume more than a billion hours of content a week on X1. Our team consumes X1 telemetry at a rate of more than 25TBs of data per day and uses this data to inform our product teams members about the performance of and engagement with the platform. We also use this data to research customer behaviors to help better inform our product team members about areas of opportunity in our products, which range from fixing bugs to creating new features. To power these insights, we need to have a reliable real-time data pipelines to deliver these insights, and we need our data scientists and data engineers to be able to quickly and efficiently be able to develop and commit new code to ensure we can measure new features the product teams are developing. To do this in an environment at this scale, we have been using Databricks, and Databricks delta to gain operational efficiencies, optimization and cost savings. Some of the features from delta that we took advantage of to achieve the desired levels of efficiencies, optimization and cost savings are: · Distributed writes to s3 (essentially eliminating 500 errors) · s3 log with fast reads and ACID transactions (massive increases in s3 scans/reads, and enabling consistent views of the bucket/table) · Vacuum · Pptimize (which has allowed us to reduce a 640 node job to 40, and massively increase efficiencies of our clusters as well as our DS/DE’s)"
Building Sessionization Pipeline at Scale with Databricks Delta
Building Sessionization Pipeline at Scale with Databricks Delta
Databricks
"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo. Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow). Key Takeaways: How Databricks & Snowflake work; Why they're so powerful; How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"
Databricks + Snowflake: Catalyzing Data and AI Initiatives
Databricks + Snowflake: Catalyzing Data and AI Initiatives
Databricks
Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a “pure SaaS” company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems. As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark. In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism’s Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support interactive user experience. Finally, we will share the future challenges for 2018 and beyond.
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...
Databricks
Learn more about Redash from our summit keynote during this product walkthrough. Redash makes it easy to explore, query, visualize, and share data.
Redash: Open Source SQL Analytics on Data Lakes
Redash: Open Source SQL Analytics on Data Lakes
Databricks
In this talk, we’ll compare different data privacy techniques & protection of personally identifiable information and their effects on statistical usefulness, re-identification risks, data schema, format preservation, read & write performance. We’ll cover different offense and defense techniques. You’ll learn what k-anonymity and quasi-identifier are. Think of discovering the world of suppression, perturbation, obfuscation, encryption, tokenization, watermarking with elementary code examples, in case no third-party products cannot be used. We’ll see what approaches might be adopted to minimize the risks of data exfiltration.
Data Privacy with Apache Spark: Defensive and Offensive Approaches
Data Privacy with Apache Spark: Defensive and Offensive Approaches
Databricks
Spark is in-memory, Redis is in-memery. The Spark-Redis connector gives Spark access to Redis' data structures as RDDs. Redis, with its blazing fast performance and optimized in-memory data structures, reduces Spark processing time by up to 98%. In this talk, Dave will share the top use cases for Spark-Redis such as time-series, recommendations and real-time bid management.
Big Data Day LA 2016/ NoSQL track - Analytics at the Speed of Light with Redi...
Big Data Day LA 2016/ NoSQL track - Analytics at the Speed of Light with Redi...
Data Con LA
In Spark 2.0, we introduced Structured Streaming, which allows users to continually and incrementally update your view of the world as new data arrives, while still using the same familiar Spark SQL abstractions. I talk about progress we’ve made since then on robustness, latency, expressiveness and observability, using examples of production end-to-end continuous applications.
Insights Without Tradeoffs Using Structured Streaming keynote by Michael Armb...
Insights Without Tradeoffs Using Structured Streaming keynote by Michael Armb...
Spark Summit
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.
Intro to databricks delta lake
Intro to databricks delta lake
Mykola Zerniuk
Columbia is a data-driven enterprise, integrating data from all line-of-business-systems to manage its wholesale and retail businesses. This includes integrating real-time and batch data to better manage purchase orders and generate accurate consumer demand forecasts.
Columbia Migrates from Legacy Data Warehouse to an Open Data Platform with De...
Columbia Migrates from Legacy Data Warehouse to an Open Data Platform with De...
Databricks
Organizations are adopting data digitization and data-driven decision making is at the heart of this transformation. Cloud Data Lakes and Datawarehouses provide great flexibility to proto-type and roll out applications continuously at much lower costs. Transactional databases are optimized for processing huge volumes of transactions in real-time, whereas the cloud data lake needs to be optimized for analyzing huge volumes of data quickly. This brings about a challenge in creating a streamlined data flow process from capturing realtime transactions into a cloud datawarehouse to drive realtime insights in a scalable and cost effective manner. In this session, we’ll show how organizations can easily overcome that challenge by adopting a robust platform with StreamSets and Delta Lake. StreamSets provides a no-code framework to automate ingestion of transactional data and data processing on Spark, while Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
Power Your Delta Lake with Streaming Transactional Changes
Power Your Delta Lake with Streaming Transactional Changes
Databricks
This ebook deep dives into Apache Spark optimizations that improve performance, reduce costs and deliver unmatched scale https://www.qubole.com/resources/ebooks/accelerating-time-to-value-of-big-data-of-apache-spark
Ebooks - Accelerating Time to Value of Big Data of Apache Spark | Qubole
Ebooks - Accelerating Time to Value of Big Data of Apache Spark | Qubole
Vasu S
Presentation on using Spark for Structure Streaming of data from Apache Kafka or Event Hubs for Kafka.
Spark Streaming with Azure Databricks
Spark Streaming with Azure Databricks
Dustin Vannoy
<p>In this talk, we will highlight major efforts happening in the Spark ecosystem. In particular, we will dive into the details of adaptive and static query optimizations in Spark 3.0 to make Spark easier to use and faster to run. We will also demonstrate how new features in Koalas, an open source library that provides Pandas-like API on top of Spark, helps data scientists gain insights from their data quicker.</p>
New Developments in the Open Source Ecosystem: Apache Spark 3.0, Delta Lake, ...
New Developments in the Open Source Ecosystem: Apache Spark 3.0, Delta Lake, ...
Databricks
We present our solution for building an AI Architecture that provides engineering teams the ability to leverage data to drive insight and help our customers solve their problems. We started with siloed data, entities that were described differently by each product, different formats, complicated security and access schemes, data spread over numerous locations and systems.
Democratizing Data
Democratizing Data
Databricks
At Abnormal Security, Spark has played a fundamental role in helping us create an ML system that detects thousands of sophisticated email threats every day. Initially, we set up our Spark infrastructure using YARN on EMR because we had previous experience with it. But after growing very quickly in a short amount of time, we found ourselves spending too much time solving problems with our Spark infrastructure and less time solving problems for our customers. Given we’re in a high growth environment where the only constant is change, we asked ourselves: aren’t these problems only going to get worse as we add more employees, more products, and more data? Over the past few months, Abnormal Security executed a full migration of our Spark infrastructure to Databricks, not only improving cost, operational overhead, and developer productivity, but simultaneously laying the foundation for a modern Data Platform via the Lakehouse architecture. In this talk, we’ll cover how we executed the migration in a few months’ time, from pre-Databricks-POC, through the POC, through the migration itself. We’ll talk about how to really figure out exactly what it is that you care about when evaluating Databricks, splitting out the must-haves from the nice-to-haves, so that you can best utilize the Databricks trial and have concrete and measurable results. We’ll talk about the work we did to execute the migration and the tooling we created to minimize downtime and properly measure performance and cost. Finally, we’ll talk about how the move to Databricks not only solved problems with our legacy Spark infrastructure, but will save us huge amounts of time in the long-run from adopting a Lakehouse architecture.
Migrating Your Data Platform At a High Growth Startup
Migrating Your Data Platform At a High Growth Startup
Databricks
Intuit Analytics Cloud 101
Intuit Analytics Cloud 101
Intuit Analytics Cloud 101
DataWorks Summit/Hadoop Summit
La actualidad más candente
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August 2016 HUG: Open Source Big Data Ingest with StreamSets Data Collector
August 2016 HUG: Open Source Big Data Ingest with StreamSets Data Collector
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Delta Lake: Open Source Reliability w/ Apache Spark
Delta Lake: Open Source Reliability w/ Apache Spark
SQL Analytics Powering Telemetry Analysis at Comcast
SQL Analytics Powering Telemetry Analysis at Comcast
Building Sessionization Pipeline at Scale with Databricks Delta
Building Sessionization Pipeline at Scale with Databricks Delta
Databricks + Snowflake: Catalyzing Data and AI Initiatives
Databricks + Snowflake: Catalyzing Data and AI Initiatives
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...
Redash: Open Source SQL Analytics on Data Lakes
Redash: Open Source SQL Analytics on Data Lakes
Data Privacy with Apache Spark: Defensive and Offensive Approaches
Data Privacy with Apache Spark: Defensive and Offensive Approaches
Big Data Day LA 2016/ NoSQL track - Analytics at the Speed of Light with Redi...
Big Data Day LA 2016/ NoSQL track - Analytics at the Speed of Light with Redi...
Insights Without Tradeoffs Using Structured Streaming keynote by Michael Armb...
Insights Without Tradeoffs Using Structured Streaming keynote by Michael Armb...
Intro to databricks delta lake
Intro to databricks delta lake
Columbia Migrates from Legacy Data Warehouse to an Open Data Platform with De...
Columbia Migrates from Legacy Data Warehouse to an Open Data Platform with De...
Power Your Delta Lake with Streaming Transactional Changes
Power Your Delta Lake with Streaming Transactional Changes
Ebooks - Accelerating Time to Value of Big Data of Apache Spark | Qubole
Ebooks - Accelerating Time to Value of Big Data of Apache Spark | Qubole
Spark Streaming with Azure Databricks
Spark Streaming with Azure Databricks
New Developments in the Open Source Ecosystem: Apache Spark 3.0, Delta Lake, ...
New Developments in the Open Source Ecosystem: Apache Spark 3.0, Delta Lake, ...
Democratizing Data
Democratizing Data
Migrating Your Data Platform At a High Growth Startup
Migrating Your Data Platform At a High Growth Startup
Intuit Analytics Cloud 101
Intuit Analytics Cloud 101
Último
Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Enterprise Knowledge
Explore the leading Large Language Models (LLMs) and their capabilities with a comprehensive evaluation. Dive into their performance, architecture, and applications to gain insights into the state-of-the-art in natural language processing. Discover which LLM best suits your needs and stay ahead in the world of AI-driven language understanding.
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
ChristopherTHyatt
Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA. In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability. In this session, participants gained insights about the following: Most common types of AI categories and use cases; Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives; Taxonomy and ontology design considerations and best practices; Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and Tools, roles, and skills to design and implement AI-powered search solutions.
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
Enterprise Knowledge
Presentation from Melissa Klemke from her talk at Product Anonymous in April 2024
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
Product Anonymous
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08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
Delhi Call girls
With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
Read about the journey the Adobe Experience Manager team has gone through in order to become and scale API-first throughout the organisation.
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Radu Cotescu
As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc
Breathing New Life into MySQL Apps With Advanced Postgres Capabilities
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
RTylerCroy
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
naman860154
What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
HampshireHUG
These are the slides delivered in a workshop at Data Innovation Summit Stockholm April 2024, by Kristof Neys and Jonas El Reweny.
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Neo4j
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
naman860154
Presented by Mike Hicks
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
ThousandEyes
If you are a Domino Administrator in any size company you already have a range of skills that make you an expert administrator across many platforms and technologies. In this session Gab explains how to apply those skills and that knowledge to take your career wherever you want to go.
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
Gabriella Davis
Slides from the presentation on Machine Learning for the Arts & Humanities seminar at the University of Bologna (Digital Humanities and Digital Knowledge program)
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Maria Levchenko
Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
UK Journal
45-60 minute session deck from introducing Google Apps Script to developers, IT leadership, and other technical professionals.
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
wesley chun
writing some innovation for development and search
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
sudhanshuwaghmare1
Digital Global Overview Report 2024 Slides presentation for Event presented in 2024 after compilation of data around last year.
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
hans926745
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Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
Data Pipelines With Streamsets
1.
Data Pipelines With Streamsets Jowanza
Joseph @jowanza
2.
Agenda About me The Problem
Space Streaming StreamSets Demo Questions
3.
About Me Software Engineer
at One ClickRetail Scala / Spark / Mesos / Kubernetes Author: Apache Spark Fieldbook Cyclist Husband and father
4.
5.
Retail Intelligence
6.
Data Size
7.
Real-Time
8.
Operational Complexity
9.
10.
Batch Processing
11.
What Are Data Pipelines?
12.
13.
What Problems Do They
Solve?
14.
Scalability Complexity Observability Extendability
15.
Lambda Architecture
16.
Kappa Architecture
17.
18.
Goals Data Provenance Guaranteed Delivery Configurable Extendable Multi-Protocol
Support DAG Distribute
19.
20.
Based on Streams
21.
Architecture
22.
Running on Mesos
23.
Analytics Data
24.
Real-Time Data
25.
Our Use Case
26.
Demo
Descargar ahora