Integrated Data Warehouse with Hadoop and Oracle Database
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This document discusses building an integrated data warehouse with Oracle Database and Hadoop. It provides an overview of big data and why data warehouses need Hadoop. It also gives examples of how Hadoop can be integrated into a data warehouse, including using Sqoop to import and export data between Hadoop and Oracle. Finally, it discusses best practices for using Hadoop efficiently and avoiding common pitfalls when integrating Hadoop with a data warehouse.
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Integrated Data Warehouse with Hadoop and Oracle Database
- 1. Building the Integrated Data Warehouse With Oracle Database and Hadoop Gwen Shapira, Senior Consultant
- 2. Why Pythian • Recognized Leader: • Global industry leader in data infrastructure managed services and consulting with expertise in Oracle, Oracle Applications, Microsoft SQL Server, MySQL, big data and systems administration • Work with over 200 multinational companies such as Forbes.com, Fox Sports, Nordion and Western Union to help manage their complex IT deployments • Expertise: • One of the world’s largest concentrations of dedicated, full-time DBA expertise. Employ 8 Oracle ACEs/ACE Directors • Hold 7 Specializations under Oracle Platinum Partner program, including Oracle Exadata, Oracle GoldenGate & Oracle RAC • Global Reach & Scalability: • 24/7/365 global remote support for DBA and consulting, systems administration, special projects or emergency response © 2012 – Pythian
- 3. About Gwen Shapira • Oracle ACE Director • 13 Years with pager • 7 as Oracle DBA • Senior Consultant: • Has MacBook, will travel. • @gwenshap • http://www.pythian.com/news/author/ shapira/ © 2012 – Pythian
- 4. Agenda • What is Big Data? • Why do we care about Big Data? • Why your DWH needs Hadoop? • Examples of Hadoop in the DWH • How to integrate Hadoop into your DWH • Avoiding major pitfalls © 2012 – Pythian
- 5. What is Big Data?
- 6. MORE DATA THAN YOU CAN HANDLE © 2012 – Pythian
- 7. MORE DATA THAN RELATIONAL DATABASES CAN HANDLE © 2012 – Pythian
- 8. MORE DATA THAN RELATIONAL DATABASES CAN HANDLE CHEAPLY © 2012 – Pythian
- 9. Data Arriving at fast Rates Typically unstructured Stored without aggregation Analyzed in Real Time For Reasonable Cost © 2012 – Pythian
- 10. Where does Big Data come from? • Social media • Enterprise transactional data • Consumer behaviour • Multimedia • Sensors and embedded devices • Network devices © 2012 – Pythian
- 11. Why all the Excitement? © 2012 – Pythian
- 12. Complex Data Architecture © 2012 – Pythian
- 13. Your DWH needs Hadoop
- 14. Big Problems with Big Data • It is: • Unstructured • Unprocessed • Un-aggregated • Un-filtered • Repetitive • And generally messy. Oh, and there is a lot of it. © 2012 – Pythian
- 15. Technical Challenges • Storage capacity • Storage throughput Scalable storage • Pipeline throughput • Processing power • Parallel processing Massive Parallel Processing • System Integration • Data Analysis Ready to use tools © 2012 – Pythian
- 16. Hadoop Principles Bring Code to Data Share Nothing © 2012 – Pythian
- 17. Hadoop in a Nutshell HDFS: Map-Reduce: Replicated Distributed Big-Data File Framework for writing massively parallel jobs System © 2012 – Pythian
- 18. Hadoop Benefits • Reliable solution based on unreliable hardware • Designed for large files • Load data first, structure later • Designed to maximize throughput of large scans • Designed to maximize parallelism • Designed to scale • Flexible development platform • Solution Ecosystem © 2012 – Pythian
- 19. Hadoop Limitations • Hadoop is scalable but not fast • Batteries not included • Instrumentation not included either • Well-known reliability limitations © 2012 – Pythian
- 20. Hadoop in the Data Warehouse Use Cases and Customer Stories
- 21. ETL for Unstructured Data Logs Flume Hadoop Web servers, Cleanup, app server, aggregation clickstreams Longterm storage DWH BI, batch reports © 2012 – Pythian
- 22. ETL for Structured Data Sqoop, Hadoop OLTP Oracle, Perl Transformation MySQL, aggregation Informix… Longterm storage DWH BI, batch reports © 2012 – Pythian
- 23. Bring the World into Your Datacenter © 2012 – Pythian
- 24. Rare Historical Report © 2012 – Pythian
- 25. Find Needle in Haystack © 2012 – Pythian
- 26. We are not doing SQL anymore © 2012 – Pythian
- 27. Connecting the (big) Dots
- 28. Sqoop Queries © 2012 – Pythian
- 29. Sqoop is Flexible (for import) • Select columns from table where condition • Or write your own query • Split column • Parallel • Incremental • File formats © 2012 – Pythian
- 30. Sqoop Import Examples • Sqoop import -‐-‐connect jdbc:oracle:thin:@//dbserver: 1521/masterdb -‐-‐username hr -‐-‐table emp -‐-‐where “start_date ’01-‐01-‐2012’” • Sqoop import jdbc:oracle:thin:@//dbserver:1521/masterdb -‐-‐username myuser -‐-‐table shops -‐-‐split-‐by shop_id Must be indexed or -‐-‐num-‐mappers 16 partitioned to avoid 16 full table scans © 2012 – Pythian
- 31. Less Flexible Export • 100row batch inserts • Commit every 100 batches • Parallel export • Update mode Example: sqoop export -‐-‐connect jdbc:oracle:thin:@//dbserver:1521/masterdb -‐-‐table bar -‐-‐export-‐dir /results/bar_data © 2012 – Pythian
- 32. Fuse-DFS • Mount HDFS on Oracle server: • sudo yum install hadoop-0.20-fuse • hadoop-fuse-dfs dfs:// name_node_hostname:namenode_port mount_point • Use external tables to load data into Oracle • File Formats may vary • All ETL best practices apply © 2012 – Pythian
- 33. Oracle Loader for Hadoop • Load data from Hadoop into Oracle • Map-Reduce job inside Hadoop • Converts data types. • Partitions and sorts • Direct path loads • Reduces CPU utilization on database © 2012 – Pythian
- 34. Oracle Direct Connector to HDFS • Create external tables of files in HDFS • PREPROCESSOR HDFS_BIN_PATH:hdfs_stream • All the features of External Tables • Tested (by Oracle) as 5 times faster (GB/s) than FUSE-DFS © 2012 – Pythian
- 35. Big Data Appliance and Exadata © 2012 – Pythian
- 36. How not to Fail
- 37. Data that belongs in RDBMS © 2012 – Pythian
- 38. Prepare for Migration © 2012 – Pythian
- 39. Use Hadoop Efficiently • Understand your bottlenecks: • CPU, storage or network? • Reduce use of temporary data: • All data is over the network • Written to disk in triplicate. • Eliminate unbalanced workloads • Offload work to RDBMS • Fine-tune optimization with Map-Reduce © 2012 – Pythian
- 40. Your Data is NOT as BIG as you think © 2012 – Pythian
- 41. Getting Started • Pick a Business Problem • Acquire Data • Use right tool for the job • Hadoop can start on the cheap • Integrate the systems • Analyze data • Get operational © 2012 – Pythian
- 42. Thank you and QA To contact us… [email protected] 1-877-PYTHIAN To follow us… http://www.pythian.com/news/ http://www.facebook.com/pages/The-Pythian-Group/163902527671 @pythian @pythianjobs http://www.linkedin.com/company/pythian © 2012 – Pythian