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How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million

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A Fortune 100 company recently introduced Hadoop into their data warehouse environment and ETL workflow to save $30 Million. This session examines the specific use case to illustrate the design considerations, as well as the economics behind ETL offload with Hadoop. Additional information about how the Hadoop platform was leveraged to support extended analytics will also be referenced.

Publié dans : Technologie, Business
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How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million

  1. 1. 1©MapR Technologies. All rights reserved. How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million Rob Rosen Sr. Director, Americas Systems Engineering MapR Technologies
  2. 2. 2©MapR Technologies. All rights reserved. MapR Overview  Enterprise-grade platform for Hadoop  Deployed at thousands of companies – Including 12 of the Fortune 100  MapR is the preferred analytics platform – Hundreds of billions of events daily – 90% of the world’s Internet population monthly – $1 trillion in retail purchases annually
  3. 3. 3©MapR Technologies. All rights reserved. Arrival of Big Data Impacts Data Warehouse Data Warehouse Volume Variety Velocity Prohibitively expensive storage costs Inability to process unstructured formats Faster arrival and processing needs
  4. 4. 4©MapR Technologies. All rights reserved. Top Concern for Big Data Multiple data sources Multiple technologies Multiple copies of data “Too many different types, sources, and formats of critical data”
  5. 5. 5©MapR Technologies. All rights reserved. The Hadoop Advantage  Fueling an industry revolution by providing infinite capability to store and process Big Data  Expanding analytics across data types  Compelling economics – 20 to 100X more cost effective than alternatives Pioneered at
  6. 6. 6©MapR Technologies. All rights reserved. Important Drivers for Hadoop  Data on compute drives efficiencies and better analytics  With Hadoop you don’t need to know what questions to ask beforehand  Simple algorithms on Big Data outperform complex models  Powerful ability to analyze unstructured data
  7. 7. 7©MapR Technologies. All rights reserved. Hadoop is the Technology of Choice for Big Data
  8. 8. 8©MapR Technologies. All rights reserved. Source Data Social Media, Web Logs Machine Device, Scientific Documents and Emails Batch ETL Transactions, OLTP, OLAP Enterprise Data Warehouse Raw data or infrequently used data consuming capacity Batch windows hitting their limits putting SLAs at risk Databases and data warehouses are exceeding their capacity too quickly How Do You Lower and Control Data Warehouse Costs? Datamarts ODS Traditional Targets
  9. 9. 9©MapR Technologies. All rights reserved. Source Data Traditional Targets Social Media, Web Logs Machine Device, Scientific Documents and Emails Transactions, OLTP, OLAP Enterprise Data Warehouse Lower Data Management Costs RDBMS MDM
  10. 10. 10©MapR Technologies. All rights reserved. Bottom-Line Impact Sensor Data Web Logs Hadoop RDBMS Benefits:  Both structured and unstructured data  Expanded analytics with MapReduce, NoSQL, etc. DW Query + PresentETL + Long Term StorageETL + Long Term Storage Solution Cost / Terabyte Hadoop Advantage Hadoop $333 Teradata Warehouse Appliance $16,500 50x savings Oracle Exadata $14,000 42x savings IBM Netezza $10,000 30x savings
  11. 11. 11©MapR Technologies. All rights reserved. What is the Best Way to Deploy Hadoop? vs. • Highly available and fully protected data • Works with existing tools • Real-time ingestion and extraction • Archive data from data warehouse Transitory Data Store • No long-term scale advantages • Unprotected data • ETL Tool focus Permanent Data Store Enterprise Data Hub
  12. 12. 12©MapR Technologies. All rights reserved. An Enterprise Data Hub  Combine different data sources  Minimize data movement  One platform for analytics Sales SCM CRM Public Web Logs Production Data Sensor DataClick Streams Location Social Media Billing Enterprise Data Hub
  13. 13. 13©MapR Technologies. All rights reserved. Key Elements of Enterprise Data Hub 99.999% HA Data Protection Disaster Recovery Scalability & Performance Enterprise Integration Multi- tenancy Enterprise-grade platform for the long term • Reliability to support stringent SLAs • Protection from data loss and user or application errors • Support business continuity and meet recovery objectives
  14. 14. 14©MapR Technologies. All rights reserved. High Availability and Dependability Reliable Compute Dependable Storage  Automated stateful failover  Automated re-replication  Self-healing from HW and SW failures  Load balancing  Rolling upgrades  No lost jobs or data  99999s of uptime • Business continuity with snapshots and mirrors • Recover to a point in time • End-to-end check summing • Strong consistency • Data safe • Mirror across sites to meet Recovery Time Objectives
  15. 15. 15©MapR Technologies. All rights reserved. Enterprise Data Hub Supports a Range of Applications 99.999% HA Data Protection Disaster Recovery Scalability & Performance Enterprise Integration Multi- tenancy Batch Interactive Real-time Self-healing Instant recovery Snapshots for point in time recovery from user or application errors Unlimited files & tables Record setting performance Direct data ingestion and access Fully compliant ODBC access and SQL-92 support Mirroring across clusters and the WAN Secure access to multiple users and groups
  16. 16. 16©MapR Technologies. All rights reserved. Business Impact  Saved millions in TCO  10x faster, 100x cheaper  Maintain the same SLAs  Implemented the change without impacting users Summary
  17. 17. 17©MapR Technologies. All rights reserved. Q & A Engage with us! @mapr mapr- technologies maprtech MapR maprtech rrosen@maprtech.com