The DAP - Where YARN, HBase, Kafka and Spark go to Production
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The document summarizes the Cask Data Application Platform (CDAP), which provides an integrated framework for building and running data applications on Hadoop and Spark. It consolidates the big data application lifecycle by providing dataset abstractions, self-service data, metrics and log collection, lineage, audit, and access control. CDAP has an application container architecture with reusable programming abstractions and global user and machine metadata. It aims to simplify deploying and operating big data applications in enterprises by integrating technologies like YARN, HBase, Kafka and Spark.
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Big Data Applications Meetup, 09/14/2016
Palo Alto, CA
More info here: http://www.meetup.com/BigDataApps/
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About the talk:
As data lake sizes grow, and more users begin exploring and including that data in their everyday analysis, keeping track of the sources for data becomes critical. Understanding how a dataset was generated and who is using it allows users and companies to ensure their analysis is leveraging the most accurate and up to date information. In this talk, we will explore the different techniques available to keep track of your data in your data lake and demonstrate how we at Cask approached and attempted to mitigate this issue.
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Date: 08/10/2016
Link to video recording: https://www.youtube.com/watch?v=XUkANr9iag0
In this webinar, Nitin Motgi, CTO of Cask, walks through the new capabilities of CDAP 3.5 and explains how your organization can benefit.
Some of the highlights include:
- Enterprise-grade security - Authentication, authorization, secure keystore for storing configurations. Plus integration with Apache Sentry and Apache Ranger.
- Preview mode - Ability to preview and debug data pipelines before deploying them.
- Joins in Cask Hydrator - Capabilities to join multiple data sources in data pipelines
- Real-time pipelines with Spark Streaming - Drag & drop real-time pipelines using Spark Streaming.
- Data usage analytics - Ability to report application usage of data sets.
- And much more!
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Talk Overview:
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Walk through ETL of the Reddit dataset. SparkSQL Analytics + Visualizations of the Dataset using MatplotLibSentiment Analysis on Reddit Comments
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Enterprises have successfully tested Apache Spark for its versatility and strengths as a distributed computing framework that can completely handle all needs for data processing, analytics, and machine learning workloads.
Since the Hadoop distributions and the public cloud already include Apache Spark, there is nothing new to be procured. However, the skills required to put Spark to good use are typically unavailable today.
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