1. The New Possibilities in Microsoft Business Intelligence
Johan Åhlén & Tim Peterson, SolidQ
Guest speakers: Tim Mallalieu & Miguel Llopis, Microsoft

2. "Information is the Oil of the 21st Century - BI and Analytics are the Refinery” (Gartner)

3. Presenters Johan Åhlén SolidQ Mentor & Sweden CEO
President, Swedish SQL Server User Group
Microsoft SQL Server MVP
Blog:
Tim Peterson
SolidQ Mentor & Nordic Board Member
Co-author of the SSAS 2008 R2 Maestros course
Blog:

4. Data Explorer presenters
Timothy Mallalieu
Group Program Manager, Cloud Data Services Team
Microsoft
Blog:
Miguel Llopis
Program Manager, Cloud Data Services Team
Blog:

5. Challenge: New data sources
August 2, 2018
Challenge: New data sources
VISION/ STRATEGY
TOMORROW
TODAY
How can we continue to succeed ?
YESTERDAY
How does the customer see us ?
What was the result ?
Efficient processes?
Social Media Competitor Data etc
Business Processes Customers
Finance
© 2004 IFS AB. All rights reserved.

6. Challenge: Data explosion
World wide information stored volume is at least doubling each year. (EMC)
87% of performance issues in application databases are related in some way to data growth. (OAUG)

7. Challenge: The BI dilemma
Scorecards and Dashboards
Management’s Perceived value
Developer’s Effort
Operational Analytics
Data Warehouse / ETL

8. The New Possibilities New Data Sources Big Data Self-service BI
Windows Azure Marketplace
Codename Data Explorer
Big Data
PDW
Hadoop (not covered in this session)
Self-service BI
PowerPivot
Power View

9. End-to-end self service BI
DEMO

10. The Business Intelligence Semantic Model
The Past - The Unified Data Model (UDM) in Analysis Services 2005/2008
The Future – The Business Intelligence Semantic model in Analysis Services 2012
Multidimensional model
Tabular model

11. Upgrading to BISM Upgrading to 2012 BISM Multidimensional
Almost no change from Analysis Services 2008
No preparation needed
Some improvements
Upgrading to 2012 BISM Tabular
Very different structure
Standard recommendation – start over!

12. Tabular/Multidimensional Differences
Calculations
MDX
DAX
Querying
DAX or MDX
Use with Crescent No
Yes
In-Memory
Yes - as option
Aggregations
Yes (optional)
Querying Relational Database
Yes - as option (ROLAP/HOLAP)
Yes -as option (Direct Query)
Client Choice Direct Query

13. Multidimensional/Tabular Advantages
Speed
X – When In-Memory
Scalability
X – MOLAP scales more than Vertipaq
Ease of Use
X – More like relational, DAX like Excel formulas, less tuning needed
Migration from AS2008
X – Almost no change
Integration with PowerPivot in Excel
X – Uses the same Vertipaq engine

14. Advantages (Continued)
Multidimensional
Tabular
Use with Crescent
X – Only option for now
Multidimensional Logic
X – More with MDX
Querying Relational Database – Ease of Use
X – Direct Query appears to be easier than ROLAP
Querying Relational Database - Logic
X – Direct Query supports limited DAX logic

15. Migrating from AS2008 Cubes to 2012 BISM Tabular Model
DEMO

16. The Parallel Data Warehouse
Large capacity data warehouse
100’s of terabytes
Massive Parallel Processing
Sold as an appliance
Software/hardware package
Multiple servers running the SQL Server database engine
Pre-configured, centrally managed, so it is manageable

17. PDW Configuration Control Rack 1-4 Data Racks Control nodes
Management Nodes
Landing Zone
Backup Nodes
1-4 Data Racks
Compute Nodes
Storage Nodes

18. PDW Data Racks
Each rack has 10 active nodes and 1 passive node (in case one of the other nodes fails)
Each node has 16 processors
Each node receives 8 distributions (instances of a distribute table)
A full 4 data rack system has 320 distributions
4 racks X 10 nodes X 8 distributions

19. How the processing is distributed
Replicated Tables
Full copy with all data created on every node
Used for dimension tables
Distributed Tables
Table created on every node, each with a portion of the data
Data divided as evenly as possible
Use a hash function on a key with a large number of values
Used for fact tables (and very large dimension tables)

20. Three Types of PDW Joins
Ultra Shared-Nothing Join
Join made between a distributed table and a repliated table
Fully local on every node
Shared-Nothing Join
Join made between two distributed tables with compatible distribution keys
Redistribution (or Shuffle) Join
Join made between two distributed tables that do not have compatible distribution keys

21. Speed of Joins
At TechNet in May a demo was done comparing a Shared-Nothing Join and a Redistribution Join
6 billion rows joined with 1.5 billion rows
Only difference between the two demos was that one had compatible distribution keys and the other did not
Shared-Nothing Join took 3 seconds
Redistribution Join took 3 minutes

22. PDW Database Design
If you have a multidimensional data structure (star schema), your design is almost done
Replicate the dimension tables
Distribute the fact tables
If you have one large dimension table, you can distribute the fact tables along the same key as the dimension table
You will still have excellent performance

23. How Do You Get Speed in Retrieving Data?
Create good indexes
Put data into a multidimensional database
Add aggregation tables in the relational database or aggregations in the multidimensional database
Create a better type of index for data retrieval (columnar)
Put all the data into memory and compress it (Vertipaq)

24. Speed – The PDW Solution
Use Massively Parallel Processing
Divide the data into small parts
Retrieve the data from each of the parts
Combine all the results together
MPP gives the most effective result when you have a very large amount of data
And you can still use indexes to improve performance further
Columnar indexes in Denali

25. Using PDW with Analysis Services
Using the multidimensional model with ROLAP
Using the multidimensional model with HOLAP
Using the tabular model with Direct Query

26. Microsoft’s Vision for Cloud Data Services
8/2/2018
Any Data, Any Size, Anywhere
Connecting With The World’s Data
Immersive Experiences, Wherever You Are

27. Microsoft Codename “Data Explorer”
Add & Manage
Data Sources
Classify
Understand
Recommend
Transform
Mash up
Cleanse
Snapshot
Publish
Sell

28. Demo - codename “Data Explorer”

29. Learn more Power View Migrating to BISM Tabular
Migrating to BISM Tabular
Link to Tim’s whitepaper
Windows Azure Data Market
Parallel Data Warehouse (PDW)
Codename “Data Explorer”

30. For attending this session and PASS SQLRally Nordic 2011, Stockholm
THANK YOU!
For attending this session and PASS SQLRally Nordic 2011, Stockholm