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Apache Atlas: Tracking dataset lineage across Hadoop components

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Apache Atlas: Tracking dataset lineage across Hadoop components

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Apache Atlas: Tracking dataset lineage across Hadoop components

  1. 1. Apache Atlas: Tracking dataset lineage across Hadoop components
  2. 2. 2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Disclaimer This document may contain product features and technology directions that are under development, may be under development in the future or may ultimately not be developed. Project capabilities are based on information that is publicly available within the Apache Software Foundation project websites ("Apache"). Progress of the project capabilities can be tracked from inception to release through Apache, however, technical feasibility, market demand, user feedback and the overarching Apache Software Foundation community development process can all effect timing and final delivery. This document’s description of these features and technology directions does not represent a contractual commitment, promise or obligation from Hortonworks to deliver these features in any generally available product. Product features and technology directions are subject to change, and must not be included in contracts, purchase orders, or sales agreements of any kind. Since this document contains an outline of general product development plans, customers should not rely upon it when making purchasing decisions.
  3. 3. 3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Speakers Andrew Ahn Governance Director Product Management
  4. 4. 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Agenda • Atlas Overview • Near term roadmap • Cross Component Lineage • Questions
  5. 5. 5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Apache Atlas Overview
  6. 6. 6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved STRUCTURED UNSTRUCTURED Vision - Enterprise Data Governance Across Platfroms TRADITIONAL RDBMS METADATA MPP APPLIANCES Project 1 Project 5 Project 4 Project 3 Metadata Project 6 DATA LAKE GOAL: Provide a common approach to data governance across all systems and data within the enterprise Transparent Governance standards and protocols must be clearly defined and available to all Reproducible Recreate the relevant data landscape at a point in time Auditable All relevant events and assets but be traceable with appropriate historical lineage Consistent Compliance practices must be consistent
  7. 7. 7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Ready for Trusted Governance OPERATIONS SECURITY GOVERNANCE STORAGE STORAGE Machine Learning Batch StreamingInteractive Search GOVERNANCE YA R N D A T A O P E R A T I N G S Y S T E M Data Management along the entire data lifecycle with integrated provenance and lineage capability Modeling with Metadata enables comprehensive data lineage through a hybrid approach with enhanced tagging and attribute capabilities Interoperable Solutions across the Hadoop ecosystem, through a common metadata store
  8. 8. 8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved DGI* Community becomes Apache Atlas May 2015 Proto-type Built Apache Atlas Incubation DGI group Kickoff Feb 2015 Dec 2014 July 2015 HDP 2.3 Foundation GA Release First kickoff to GA in 7 months Global Financial Company * DGI: Data Governance Initiative Faster & Safer Co-Development driven by customer use cases
  9. 9. 9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Apache Atlas: Metadata Services • Cross- component dataset lineage. Centralized location for all metadata inside HDP • Single Interface point for Metadata Exchange with platforms outside of HDP • Business Taxonomy based classification. Conceptual, Logical And Technical Apache Atlas Hive Ranger Falcon Sqoop Storm Kafka Spark NiFi
  10. 10. 10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Big Data Management Through Metadata Management Scalability Many traditional tools and patterns do not scale when applied to multi-tenant data lakes. Many enterprise have silo’d data and metadata stores that collide in the data lake. This is compounded by the ability to have very large windows (years). Can traditional EDW tools manage 100 million entities effectively with room to grow ? Metadata Tools Scalable, decoupled, de-centralized manage driven through metadata is the only via solution. This allows quick integration with automation and other metamodels Tags for Management, Discovery and Security Proper metadata is the foundation for business taxonomy, stewardship, attribute based security and self-service.
  11. 11. 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Apache Atlas High Level Architecture Type System Repository Search DSL Bridge Hive Storm Falcon Others REST API Graph DB Search Kafka Sqoop Connectors MessagingFramework
  12. 12. 12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Near Term Roadmap: Summer 2016
  13. 13. 13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Dynamic Access Policy Driven by metadata
  14. 14. 14 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Business Catalog Breadcrumbs for taxonomy context path Contents at taxonomy context
  15. 15. 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Hadoop Cross Component Data Lineage
  16. 16. 16 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Sqoop Teradata Connector Apache Kafka Atlas: Tracks Metadata + Lineage in one place Custom Activity Reporter Metadata Repository RDBMS
  17. 17. 17 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Technical and Logical Metadata Exchange Knowledge Store Atlas REST API Structured Unstructured Files: XML / JSON 3rd Party Vendors Custom Reporter Non-Hadoop
  18. 18. 18 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Hive Integration: Model for integration Apache Atlas Hive Bridge (Client) Hive Hook (Post-execution) REST API
  19. 19. 19 © Hortonworks Inc. 2011 – 2016. All Rights Reserved HDF: Dataflow Governance Solution
  20. 20. 20 © Hortonworks Inc. 2011 – 2016. All Rights Reserved HORTONWORKS CONFIDENTIAL & PROPRIETARY INFORMATION Dataflow Security Use case Requirements Accelerated Data Collection: An integrated, data source agnostic collection platform Increased Security and Unprecedented Chain of Custody: Secure from source to storage with high fidelity data provenance The Internet of Any Thing (IoAT): A Proven Platform for the Internet of Things http://hortonworks.com/hdf/
  21. 21. 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Enterprise Grade Governance Dataflow Solution Filtered Metadata • HDP Taxonomy • Centrallized Metadata Repository • Downstream HDP Impacts • Cross component lineage • 3rd Party integration • Guaranteed Delivery • Data Buffering • Prioritized Queueing • Flow specific QoS • Visual Command & Control Months Lineage Years Lineage Reference Taxonomy (Tags) Event level versus Dataset level HDF - NiFI Operation Control Maximum Fidelity Event Level HDP – Atlas Governance Management Medium / Low Fidelity Dataset Level
  22. 22. 23 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Demo • Tutorial • Atlas Tour • Sqoop Lineage • Kafka / Storm Linage
  23. 23. 24 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Availability: - Tech Preview VMs: May 2016 - GA Release: Summer 2016
  24. 24. 25 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Questions ?
  25. 25. 26 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Reference
  26. 26. 27 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Online Resources VM: https://s3.amazonaws.com/demo-drops.hortonworks.com/HDP- Atlas-Ranger-TP.ova —> Download Public Preview VM Tutorial: https://github.com/hortonworks/tutorials/tree/atlas-ranger- tp/tutorials/hortonworks/atlas-ranger-preview Blog: http://hwxjojo.wpengine.com/blog/the-next-generation-of- hadoop-based-security-data-governance/ (this is giving an error, right now) Learn More: http://hortonworks.com/solutions/atlas-ranger- integration/
  27. 27. 32 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Thank You

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