Making EDW More Flexible with Hadoop
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1 Making EDW More Flexible with Hadoop Rob Rosen Big Data GTM Lead Pentaho Corporation
2 The State of Data Warehouses Gartner Research Publication Date: 1 December 2010 ID Number: G Predicts 2011: Data Management Disciplines Elevate Business Criticality 2
3 TDWI Hadoop Survey: Business Intelligence and Data Warehouse 3
4 Competitive Advantage vs. Operational Efficiency Operational Efficiency Competitive Advantage 4
5 Hadoop = Infrastructure Software Flexibility Time Costs 5
6 Barriers to Implementing Hadoop Technologies It s complex & difficult, plus our executives don t understand it. Where should I start? 6
7 Use Case Scenario Call Volume Analysis VOIP service provider with a B2B customer base wants to sub-lease excess capacity on the weekends COO: what are the top 10 states for outbound calls on Fridays, Saturdays and Sundays? Detailed information available, but not in the EDW: Call records: date/timestamp & source phone # Reference data: area code by country, state & time zone (North American Numbering Plan)? 7
8 Traditional EDW Architecture Structured Data SQL or ETL Tool Extract Transform Load Data Mart(s) / Warehouse Metadata Data Integration Data acquisition & ingestion Parsing Cleansing Enrichment Data Integration Dimension management Bulk loading DB management Dashboard Report Analysis 2012, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866)
9 Challenges with the Traditional EDW EDW can t handle increasing data and workloads, so companies must: Reduce the volume of data Restrict end-user access (# of users or access windows) to accommodate longer batch processing windows Purchase additional capacity (hardware / licenses), which can be as much as $100K / TB Then, companies are faced with the following challenges: The trade-off: more data versus user-experience The incremental outlay of capital required to expand the EDW or purchase more proprietary ETL tool capacity The inability of the incumbent ETL vendor to work with Hadoop 9
10 EDW Architecture Hadoop Front-End Structured Data ETL ETL Data Mart(s) / Warehouse Metadata Dashboard Unstructured Data Data Integration Data acquisition & ingestion Data Integration Dimension management Report Analysis Parsing Bulk loading Cleansing Enrichment DB management 10
11 Data Pipeline 2012/03/06 00:00:00.000, /02/21 00:00:00.000, /03/08 00:00:00.000, /02/18 00:00:00.000, /03/08 00:00:00.000, ,2012,6,201,NJ,UNITED STATES,E,Friday,1 3,2012,6,513,OH,UNITED STATES,E,Friday,1 3,2012,6,850,FL,UNITED STATES,EC,Friday,1 3,2012,7,631,NY,UNITED STATES,E,Saturday,1 3,2012,6,650,CA,UNITED STATES,P,Friday,1 11
12 Analysis & Visualization SQL over Hadoop tool 3,2012,6,201,NJ,UNITED STATES,E,Friday,1 3,2012,6,513,OH,UNITED STATES,E,Friday,1 3,2012,6,850,FL,UNITED STATES,EC,Friday,1 3,2012,7,631,NY,UNITED STATES,E,Saturday,1 3,2012,6,650,CA,UNITED STATES,P,Friday,
13 Summary EDW Optimization is a logical first use case for Hadoop: Tremendous cost savings Revenue enhancement potential Deliver value and gain experience with Hadoop Benefits: Increased revenue AND lowered costs Archive onto lower-cost storage platform recover EDW operational headroom lower storage costs Understand transactional context to gain deeper insight into customer behavior Leverage the Ecosystem for Assistance Time Flexibility Costs 13
14 Questions and Answers Thank ofc mob 14
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