Extending the Data Warehouse with Hadoop - Hadoop world 2011
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The document discusses the integration of Hadoop into Orbitz's data architecture to enhance the enterprise data warehouse (EDW) by enabling access to large volumes of behavioral data for improved analysis and decision-making. It highlights the benefits of Hadoop for data extraction, transformation, and analysis, while also outlining the challenges faced in making data accessible to non-technical users. Ultimately, it concludes that Hadoop should be viewed as a complementary solution to existing data warehousing infrastructure, particularly for managing big data needs.
Extending the Data Warehouse with Hadoop - Hadoop world 2011
- 1. Extending the Enterprise Data Warehouse with Hadoop Robert Lancaster and Jonathan Seidman Hadoop World 2011 November 8 | 2011
- 2. Who We Are • Robert Lancaster – Solutions Architect, Hotel Supply Team – rlancaster@orbitz.com – @rob1lancaster – Co-organizer of Chicago Big Data and Chicago Machine Learning Study Group. • Jonathan Seidman – Lead Engineer, Business Intelligence/Big Data Team – Co-founder/organizer of Chicago Hadoop User Group and Chicago Big Data – jseidman@orbitz.com – @jseidman page 2
- 3. Launched in 2001 Over 160 million bookings page 3
- 4. Some History… page 4
- 5. In 2009… • The Machine Learning team is formed to improve site performance. For example, improving hotel search results. • This required access to large volumes of behavioral data for analysis. – Fortunately, the required data was collected in session data stored in web analytics logs. page 5
- 6. The Problem… • The only archive of the required data went back about two weeks. Non-transactional Data Transactional data (e.g. searches) (e.g. bookings) and aggregated Non- transactional data Data Warehouse page 6
- 7. Hadoop Provided a Solution… Detailed non- transactional data (what every user sees, clicks, etc.) Transactional data (e.g. bookings) and aggregated Non- transactional data Data Warehouse Hadoop page 7
- 8. Deploying Hadoop Enabled Multiple Applications… 100.00% Queries 90.00% 80.00% Searches 71.67% 70.00% 60.00% 50.00% 40.00% 34.30% 31.87% 30.00% 20.00% 10.00% 2.78% 0.00% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 page 8
- 9. And Useful Analyses… page 9
- 10. But Brought New Challenges… • Most of these efforts are driven by development teams. • The challenge now is unlocking the value of this data for non- technical users. page 10
- 11. In Early 2011… • Big Data team is formed under Business Intelligence team at Orbitz Worldwide. • Allows the Big Data team to work more closely with the data warehouse and BI teams. • Reflects the importance of big data to the future of the company. page 11
- 12. A View Shared Beyond Orbitz… “We strongly believe that Hadoop is the nucleus of the next- generation cloud EDW…” “…but that promise is still three to five years from fruition.”* *James Kobielus, Forrester Research, “Hadoop, Is It Soup Yet?” page 12
- 13. Two Primary Ways We Use Hadoop to Complement the EDW • Extraction and transformation of data for loading into the data warehouse – “ETL”. • Off-loading of analysis from the data warehouse. page 13
- 14. ETL Example: Proposed Dimensional Model Raw logs Hadoop Dimensional model page 14
- 15. ETL Example: Click Data Processing Web Data Server Web Cleansing Web Server Logs ETL DW (Stored DW Servers procedure) Several hours of processing ~20% original data size Current Processing in Data Warehouse page 15
- 16. ETL Example: Click Data Processing • Moving to Hadoop will facilitate: – Remove load from the data warehouse. – Adding additional attributes for processing. – Allow processing to be run more frequently. Web Data Server Web Cleansing Web Server Logs Hadoop (MapReduce) DW Servers Proposed Processing in Hadoop page 16
- 17. Analysis Example: Geo-Targeting Ads • Facilitated analysis that allows for more personalized ad content. • Allowed marketing team to analyze over a years worth of search data. • Provided analysis that was difficult to perform in the data warehouse. page 17
- 18. BI Vendors Are Working on Hadoop Integration Both big (relatively)… page 18
- 19. And small… page 19
- 20. Example Processing Pipeline for Web Analytics Data page 20
- 21. Example Use Case: Selection Errors page 21
- 22. Use Case – Selection Errors: Introduction • Multiple points of entry. • Multiple paths through site. • Goal: tie events together to form picture of customer behavior. page 22
- 23. Use Case – Selection Errors: Processing page 23
- 24. Use Case – Selection Errors: Visualization page 24
- 25. Example Use Case: Beta Data page 25
- 26. Use Case – Beta Data: Introduction • Hotel Sort Optimization • Compare A vs. B • Web Analytics Data – What user saw. – How user behaved • Server Log Data – Sorting behavior used. page 26
- 27. Use Case – Beta Data Processing page 27
- 28. Use Case – Beta Data: Visualization page 28
- 29. Example Use Case: RCDC page 29
- 30. Use Case – RCDC: Introduction • Understand and improve cache behavior. • Improve “coverage” – Traditionally search 1 page of hotels at a time. – Get “just enough” information to present to consumers. – Increase amount of availability information we have when consumer performs a search. • Data needed to support needs beyond reporting. page 30
- 31. Use Case – RCDC: Processing page 31
- 32. Use Case – RCDC: Visualization page 32
- 33. Conclusions • Hadoop market is still immature, but growing quickly. Better tools are on the way. – Look beyond the usual (enterprise) suspects. Many of the most interesting companies in the big data space are small startups. • Hadoop will not replace your data warehouse, but any organization with a large data warehouse should at least be exploring Hadoop as a complement to their BI infrastructure. page 33
- 34. Conclusions • Work closely with your existing data management teams. – Your idea of what constitutes big data might quickly diverge from theirs. • The flip-side to this is that Hadoop can be an excellent tool to off-load resource-consuming jobs from your data warehouse. page 34