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Best Practices for Implementing Enterprise BI Solution

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Learn best practices to make your organization a center of BI excellence! I’ll walk you through lessons learned during our implementation of an enterprise end-to-end BI solution, which is discussed in the Records Management Firm Saves $1 Million, Gains Faster Data Access with Microsoft BI case study published by Microsoft. Working experience with the dimensional modeling and the Microsoft BI stack is assumed..

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Best Practices for Implementing Enterprise BI Solution

  1. 1. Best Practices for Implementing Enterprise BI Solution Teo Lachev, Prologika teo.lachev@prologika.com
  2. 2. Why BI projects fail • 70-80% corporate BI projects fail (Gartner http://bit.ly/YRi028) • Top reasons  Poor communication between IT and Business  Failure to ask the right questions  Other reasons from my experience      Business doesn’t know about BI Inexperience and lack of technical knowledge “When all you have is a hammer…” Data inaccuracy Performance degradation with large datasets
  3. 3. Agenda • Share best practices and lessons learned  BI architecture  Data warehouse design  ETL  Semantic layer  Presentation layer • Assumptions  Experience with Microsoft BI and database design • Microsoft case study  Records Management Firm Saves $1 Million http://bit.ly/15exUpM  Most performance practices around biggish data
  4. 4. Ground rules • Ask questions • Turn cellphones off • Tweet away (@tlachev #BestBI)
  5. 5. About me • Consultant, author, and mentor with focus on Microsoft BI • Owner of Prologika – BI consulting and training company based in Atlanta (www.prologika.com) • Microsoft SQL Server MVP for 10 years • Leader of Atlanta BI group (atlantabi.sqlpass.org)
  6. 6. Used phased approach • Identify critical success factors • Break project into phases • Phase 1 • Most important • Scope it relatively small • Sets foundation • Business process to model • First conformant dimensions • A few fact tables
  7. 7. Use classic BI solution architecture Transactional reporting Dimension Tables Fact Tables ETL Integration Services Multidimensional OR Historical & trend reporting Tabular Data Sources Data is extracted from data sources, transformed, and loaded into DW Data Warehouse Data is stored in dimensional schema consisting of dimension and fact tables Semantic Layer Great performance Business calculations Single version of truth Client support Security Isolation Ad-hoc reports Operational reports Dashboards Third party tools Presentation Layer Standard reporting Ad-hoc reporting Dashboards
  8. 8. Keep it simple! Europe NA ASIA Europe Teo’s insight: Remove complexity until it cannot be simplified anymore Asia NA
  9. 9. Consider active-active clustering Cluster Database server SSAS server
  10. 10. Check your environment • I/O  BACKUP DATABASE [ContosoRetailDW] TO DISK='NUL';  Or use tools such as IOMeter or CrystalMark  I/O should be above 500 MB/sec • Network speed  select * from <some fact table> (consider discarding query results)  Num rows/sec = row count/execution time (sec)  Aim for > 100K rows/sec • Virtualization  Disk pass-through enabled  Dedicated resources
  11. 11. Agenda BI architecture Data warehouse design ETL Semantic Layer Presentation layer
  12. 12. Star schema is your best friend • Your dimensional model is foundation • Design it with end user in mind • Avoid normalization • Avoid summarized tables • Use smartkey (YYYYMMDD) or [date] keys for Date tables • Use referential integrity Teo’s insight: The fact that Tabular supports more flexible relationships doesn’t mean that star schema is obsolete - just the opposite.
  13. 13. Optimize physical storage • Set database recovery to Simple • Index considerations  Cluster key on DateKey column in fact tables  Other indexes as needed  File groups  File group per each large table  Files on different drives  Avoid using Primary file group
  14. 14. Use partitioning • Partition large tables (above 50 Gb)  Partition switching  Better manageability  Partition elimination when querying data Good read: “Partitioned Table and Index Strategies Using SQL Server 2008” whitepaper by Ron Talmage
  15. 15. Use compression • Consider page compression above 1 TB • 50-80% saving in disk space • To estimate storage savings:  Use SSMS Data Compression Wizard  sp_estimate_data_compression_savings stored procedure EXEC sp_estimate_data_compression_savings 'dbo', 'FactResellerSales', 1, NULL, 'PAGE' Good read: “Data Compression: Strategy, Capacity Planning and Best Practices” whitepaper by Sanjay Mishra
  16. 16. Agenda BI architecture Data warehouse design ETL Semantic Layer Presentation layer
  17. 17. Consider merge design pattern • More efficient than SSIS transforms • More flexible than SSIS lookups • Easier to maintain stored procedure with T-SQL merge statement LOB Staging Database Files select a,b from st1 inner join st2 where... incremental extraction Data Sources Staging Database work table dimension or fact table Data Warehouse
  18. 18. Consider Operational Data Store • ODS advantages • Offloads transactional data • Maintains data history • Smarter “staging” database Start_Date End_Date Store Product 1/1/2010 5/1/2010 Atlanta Mountain Bike 1 5/2/2010 3/8/2012 Atlanta Mountain Bike 2 3/9/2012 12/31/9999 Norcross Mountain Bike 2 …
  19. 19. Index considerations • Eliminate read locks • Indexes: ALLOW_PAGE_LOCKS = OFF and ALLOW_ROW_LOCKS = OFF • View hints WITH (NOLOCK) or SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED • Drop non-clustered indexes and constraints  With massive updates (10% or more)  Enables non-logged load  Consider COLUMNSTORE indexes when queries aggregate data
  20. 20. Take advantage of partitioning • Consider partition switching  Fast incremental load  Parallel partition load  Faster updates • Use Manage Partition Wizard to generate  Switch in/out scripts  Staging table  Sliding window For parallel partition load, change the table lock escalation ALTER TABLE … SET ( LOCK_ESCALATION = AUTO) To find the table lock escalation: SELECT lock_escalation_desc FROM sys.tables WHERE name = ‘<table name>'
  21. 21. Optimize big joins • Set OPTION (HASH JOIN or LOOP JOIN) http://bit.ly/108HuHR
  22. 22. Agenda BI architecture Data warehouse design ETL Semantic Layer Presentation layer
  23. 23. BI Semantic Layer Third-Party BI Applications Reporting Services Reports PowerPivot Applications Excel Workbooks MDX SharePoint Dashboards & Scorecards DAX Multidimensional Tabular MDX DAX MOLAP ROLAP xVelocity (VertiPaq) DirectQuery Files OData Feeds
  24. 24. Choose semantic layer wisely • Decision checkpoints • Data volumes • Complexity • Scenarios for considering Multidimensional  Data warehousing  Large data volumes  Complex models • Scenarios for considering Tabular  Promoting PowerPivot models to organizational models  Rapid development for simple models  Transactional reporting? (be careful)
  25. 25. Optimize Multidimensional • Don’t be afraid of biggish data • Avoid complex scope assignments • Centralize business logic • Consider fast storage • Consider single cube
  26. 26. Tabular Considerations • Improve your design experience http://bit.ly/106iKjt • Small dataset during dev • Disable automatic calculation • Remove unnecessary columns • Be careful about transactional reporting • No cross-fact table support • Performance degradation with big data - http://bit.ly/136h60U Dim Date Fact Orders Fact Receipts
  27. 27. Partition when makes sense • Partition large measure groups (above 100 million)  Mostly management technique  Useful for incremental processing  Partition slice: ~50 million • Automate with partition generator http://bit.ly/partitiongenerator • Use SQL views to wrap tables
  28. 28. When to use self-service BI? • Know your end users  Power users  Financial analysts • When self-service BI make sense?  Waiting for organizational BI to happen  Ideate and promote lateral thinking  Consider 80/20 rule  80% organizational BI  20% self-service BI
  29. 29. Agenda BI architecture Data warehouse design ETL Analytical layer Presentation layer
  30. 30. Dashboards “A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.” Stephen Few, “Information Dashboard Design” book From “Information Dashboard Design” book
  31. 31. PerformancePoint in real life
  32. 32. Power View in real life
  33. 33. Excel Services in SharePoint 2013
  34. 34. Consider your dashboard options Technology Pros Cons PerformancePoint Designed for scorecards and KPIs Supporting views (reports, Excel spreadsheets, PP reports) Decomposition tree Customizable BI pro-oriented No “wow” effect Power View Highly interactive Easy to implement End user-oriented No extensibility No mobile support yet (but promised) Currently requires Silverlight (MS working on HTML5) Excel Services Use Excel pivot reports Easy to implement Reports updatable in SP 2013 Reports not updatable in SP 2010 No “wow” effect Reporting Services reports Highly customizable Rich visualizations Require report experience Reports not updatable Drillthrough requires new reports
  35. 35. Summary • I shared proven practices and tips from past experience • Keep things simple but have sound design • How to contact me: • • • • Email: teo.lachev@prologika.com Web: www.prologika.com Blog: http://prologika.com/cs/blogs/ Newsletter: http://prologika.com/Newsroom/News.aspx

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