1. Real-Time Business Intelligence with SQL Server 2005 Analysis Services

2. What are the barriers to real-time business intelligence (BI)? How can Microsoft SQL Server 2005 Analysis Services be used to make BI more real-time? What Will We Cover?

3. Difficulties of Real-Time BISSIS POSOLTPCleanse and Enrich DW POSOLTP UDM Staging Cube Validation UDM Production Cube

4. Barriers to Real-Time BI BarrierSolution Historical view None Coordination with business processes across systems None Data quality management None Integration of multiple data sources Heterogeneous Query Processing (HQP) Data consistency Snapshot Isolation

5. More Barriers to Real-Time BI BarrierSolution Managing aggregates Isolating the OLTP system from long- running queries Data Push & Proactive Caching Knowing what has changed Notification services Linking back to the source system Actions

6. Pushing Data into UDM Data can be pushed directly into a Unified Dimensional Model SQL Server 2005 Integration Services processing transforms Includes fact and dimension tables SSIS POSOLTPCleanse and Enrich POSOLTP UDM Production Cube

7. Demo Linking Integration Services (SSIS) and Analysis Services Directly View an SSIS Package Run an SSIS Package Browse the Updated Cube demonstration

8. Updating with Trickle Feeds Trickle feeds can get data directly into the UDM Integration Services updates the cube every few minutes SSIS POSOLTP POSOLTP UDM Production Cube Cleanse and Enrich

9. Building the Cube Directly UDM can combine data from multiple sources One of the underlying sources must be SQL Server Not applicable for all scenarios SSIS POSOLTP POSOLTP UDM Production Cube Cleanse and Enrich

10. Continuously Changing Data Problem Solution How to handle updated data Source data might be continually changing How to ensure consistency during processing Use Snapshot Isolation

11. Proactive Caching Policy-based management Has source data changed? When to refresh? How to answer queries during refresh Proactive caching combines OLAP query performance Real-time data access as needed No more explicit “cube processing”

12. Proactive Caching – An ExampleUDM MOLAP Cache OLTP MDX Analysis Services

13. Proactive Caching – An ExampleUDM MOLAP Cache Events OLTP POS SQL Analysis Services

14. Proactive Caching – An ExampleUDM MOLAP Cache New Version OLTP Data Analysis Services

15. Using Policies to Refresh the CacheUDM POSOLTP POSOLTP Policy-based refresh of the cache UDM Production Cube

16. Demo Using MOLAP and Reverting to ROLAP when Latency Exceeded View Partition Settings Cause Latency Revert to ROLAP demonstration

17. Proactive Caching Challenges Efficiency How to avoid overloading Analysis Services with frequent updates How fast can the caches catch up? Performance How to balance between latency and performance Notifications Is the cache refreshed on change or periodically? How does AS know that the RDBMS has changed?

18. Policy Settings PropertyDescription SilenceInterval After an update, for how long must there be a quiet time with no further updates before rebuild starts? -1 (infinite) = no quiet time SilenceOverrideInterval If no quiet time, start anyway after this time -1 (infinite) = no override ForceRebuildInterval How long after last cache was built should rebuild of a new cache always commence? -1 (infinite) = no periodic rebuild Latency How out-of-date can the cache be before reverting to ROLAP? -1 (infinite) = never revert to ROLAP mode

19. Scaling Up Problem Solution How to handle large quantities of data Re-creating the whole cache on every change is expensive Use ROLAP Use partitions Use incremental cache updates to add data

20. Demo Using Automatic MOLAP with Polling Queries and Incremental Refresh View Cube Settings View Reports Add New Data demonstration