1. 2012 © Trivadis BASEL BERN LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MÜNCHEN STUTTGART WIEN SQL Server 2012 Parallel Data Warehouse Meinrad Weiss Principal Consultant Trivadis AG

2. 2012 © Trivadis Trivadis solution portfolio and competences Mai 2013 SQL Server 2012 Parallel Data Warehouse 2 IT SOLUTIONS, SERVICES & PRODUCTS TECHNOLOGIES Oracle, Microsoft, IBM, Open Source Integration, Application Performance Management, Security Infrastructure Engineering Application Development IT departments Business departments CUSTOMER Business Intelligence Business Integration Services Training Managed Services

3. 2012 © Trivadis Trivadis facts & figures date Trivadis – the company 3 11 Trivadis locations with more than 600 employees Financially independent and sustainably profitable Key figures 2011  Revenue CHF 104 / EUR 84 Mio.  Services for more than 800 clients in over 1,900 projects  200 Service Level Agreements  More than 4,000 training participants  Research and development budget: CHF 5.0 / EUR 4 Mio. Hamburg Dusseldorf Frankfurt Stuttgart Munich Freiburg Vienna Basel Bern Zurich Lausanne ~380 employees ~200 employees ~30 employees Mai 2013 3 SQL Server 2012 Parallel Data Warehouse

4. 2012 © Trivadis Visit us, win a price and be prepared for your next adventure SQL Server 2012 Parallel Data Warehouse Mai 2013 4

5. 2012 © Trivadis Agenda 1.Positioning Parallel Data Warehouse (PDW) 2.Architecture 3.Working with PDW 4.PDW and Big Data SQL Server 2012 Parallel Data Warehouse Mai 2013 5

6. 2012 © Trivadis SQL Server 2012 Parallel Data Warehouse Mai 2013 6

7. 2012 © Trivadis Data Warehouse – Products Positioning 1 1 2 2 Balanced solution for mostly scan-centric workloads. Max HW tune-up for most DW scenarios. 3 3 Most flexible architecture for handling all DW scenarios. Scale Complexity HA by default SW-HW integration SQL Server 2012 Fast Track SQL Server 2012 SQL Server 2012 PDW 1 1 2 2 3 3 PDW with Distributed Data Architecture SQL Server 2012 Parallel Data Warehouse Mai 2013 7

8. 2012 © Trivadis SQL Server 2012 Parallel Data Warehouse Mai 2013 8

9. 2012 © Trivadis Agenda 1.Positioning Parallel Data Warehouse (PDW) 2.Architecture 3.Working with PDW 4.PDW and Big Data SQL Server 2012 Parallel Data Warehouse Mai 2013 9

10. 2012 © Trivadis Parallel Data Warehouse at a glance Shared-nothing parallel database system  Massively parallel processing (MPP)  A “Control” server that accepts user queries, generates a plan, and distributes operations in parallel to compute nodes  Multiple “Compute” servers running SQL Server Delivered as an appliance  Balanced and pre-configured hardware and software  Scales from 2 to 56 Nodes  Fastest Time to Market SQL Server 2012 Parallel Data Warehouse Mai 2013 10

11. 2012 © Trivadis Base Unit (1/4 Rack) All hosts and VM’s run Windows Server 2012 Standard  All Fabric and workload activity happens in Hyper-V virtual machines, with Fabric VM’s sharing 1 server  Failover is handled by Hyper-V  PDW Agent runs on all hosts and all VMs, collects appliance health data on fabric and workload  Windows Storage Spaces handles mirroring and spares Host 1 Host 0 Host 2 Host 3 Storage Spaces IB & Ethernet Direct attached SAS Control FAB AD Compute 1Compute 2 SQL Server 2012 Parallel Data Warehouse Mai 2013 11 VMM

12. 2012 © Trivadis Current Limitations: Performance and Scale Up today Scale Up (and pay) Old server will be obsolete if bigger system is required $$$ Mai 2013 12 SQL Server 2012 Parallel Data Warehouse ? ?

13. 2012 © Trivadis SCALING FROM 2 TO 56 NODES Appliance can grow with increasing workload Provisioning consists of 3 phases :  Bare metal provisioning of new nodes  Provisioning of workload VMs and ‘hooking up’ to other workload VMs  Redistribution of data Host 1 Host 0 Host 2 Host 3 Storage Spaces IB & Ethernet Direct attached SAS Control FAB AD Compute 1Compute 2 Host 2 Host 3 Storage Spaces Compute 1 Compute 2 SQL Server 2012 Parallel Data Warehouse Mai 2013 13 VMM

14. 2012 © Trivadis 481216202428323640 SQL Server Compute Nodes System Throughput Regular SQL Server ( 1 Node) Seamless Scalability 5 Nodes 10 Nodes 20 Nodes 30 Nodes Mai 2013 14 SQL Server 2012 Parallel Data Warehouse

15. 2012 © Trivadis High Availability Storage Spaces manages the physical disks in the disk enclosures Failover:  One cluster across the whole appliance  VMs are automatically migrated on host failure  Affinity and anti-affinity maps enforce rules Host 1 Host 0 Host 2 Host 3 Storage Spaces IB & Ethernet Direct attached SAS Control FAB AD Compute 1Compute 2 SQL Server 2012 Parallel Data Warehouse Mai 2013 15 VMM

16. 2012 © Trivadis Agenda 1.Positioning Parallel Data Warehouse (PDW) 2.Architecture 3.Working with PDW 4.Introduction to Big Data 5.Microsoft and Big Data SQL Server 2012 Parallel Data Warehouse Mai 2013 16

17. 2012 © Trivadis Distribution and Replication of Data: Replicate Time Dim Date Dim ID Calendar Year Calendar Qtr Calendar Mo Calendar Day Store Dim Store Dim ID Store Name Store Mgr Store Size Product Dim Prod Dim ID Prod Category Prod Sub Cat Prod Desc Mktg Campaign Dim Mktg Camp ID Camp Name Camp Mgr Camp Start Camp End TDTD PDPD SDSD MDMD TDTD PDPD SDSD MDMD TDTD PDPD SDSD MDMD Smaller (<5GB ) Dimension Tables are Replicated on Every Compute Node TDTD PDPD SDSD MDMD Sales Facts Date Dim ID Store Dim ID Prod Dim ID Mktg Camp Id Qty Sold Dollars Sold Result: Fact -Dimension Joins can be performed locally SQL Server 2012 Parallel Data Warehouse Mai 2013 17

18. 2012 © Trivadis Create Replicated Table CREATE TABLE myTable ( id int NOT NULL, lastName varchar(20), zipCode varchar(6) ) WITH (CLUSTERED COLUMNSTORE INDEX); CREATE TABLE myTable ( id int NOT NULL, lastName varchar(20), zipCode varchar(6) ) WITH (CLUSTERED COLUMNSTORE INDEX);  Creates tables on each of the individual compute nodes and assigns them to the REPLICATED file group.  Data Compression is automatically turned on SQL Server 2012 Parallel Data Warehouse Mai 2013 18

19. 2012 © Trivadis TDTD PDPD SDSD MDMD TDTD PDPD SDSD MDMD TDTD PDPD SDSD MDMD TDTD PDPD SDSD MDMD Distribution and Replication of Data: Distribute SF -1 Larger (> 10 GB) Fact Table is Hash Distributed Across All Compute Nodes SF -1 SF -2 SF -3 SF -4 Time Dim Date Dim ID Calendar Year Calendar Qtr Calendar Mo Calendar Day Store Dim Store Dim ID Store Name Store Mgr Store Size Product Dim Prod Dim ID Prod Category Prod Sub Cat Prod Desc Mktg Campaign Dim Mktg Camp ID Camp Name Camp Mgr Camp Start Camp End Sales Facts Date Dim ID Store Dim ID Prod Dim ID Mktg Camp Id Qty Sold Dollars Sold SQL Server 2012 Parallel Data Warehouse Mai 2013 19

20. 2012 © Trivadis Distribution on a PDW PDW Node 1 Create Table _a Create Table _b … Create Table _h 8 Tables per Node PDW Node 2 Create Table _a Create Table _b … Create Table _h PDW Node 8 Create Table _a Create Table _b … Create Table _h PDW Node … Final Result: 64 individual tables across a 8 node (1 data rack HP) appliance CREATE TABLE myTable (column Defs) WITH (DISTRIBUTION = HASH (id)); CREATE TABLE myTable (column Defs) WITH (DISTRIBUTION = HASH (id)); Mai 2013 20 SQL Server 2012 Parallel Data Warehouse

21. 2012 © Trivadis xVelocity gives next-gen performance Lightning Fast Data Query Processing CustomerSalesCountrySupplierProducts Columnstore Provides Dramatic Performance Updateable and clustered xVelocity columnstore Stores data in columnar format Memory-optimized for next-generation performance Updateable to support bulk and/or trickle loading Save Time and Costs Real-Time DW Up to 50X Faster Up to 15x compression Mai 2013 21 SQL Server 2012 Parallel Data Warehouse

22. 2012 © Trivadis Web-Based Management Dashboard SQL Server 2012 Parallel Data Warehouse Mai 2013 22

23. 2012 © Trivadis Web-Based Management Dashboard (2) SQL Server 2012 Parallel Data Warehouse Mai 2013 23

24. 2012 © Trivadis Web-Based Management Dashboard (3) SQL Server 2012 Parallel Data Warehouse Mai 2013 24

25. 2012 © Trivadis PDW Querying 1 Petabyte of data in 1 second Mai 2013 25 SQL Server 2012 Parallel Data Warehouse 294‘000‘000‘000 rows http://www.sqlpass.org/summit/2012/DayOneKeynote.aspx

26. 2012 © Trivadis Reference Case: Today’s process flow / Building blocks DB_ GSAPOP DB_ MasterTables DB_ ReportTables FinanceCube Baseline : Once data extracted from SAP: Time taken to create end-end Reports and Cubes insights 13+ hours (In production typical 20+ hours with multiple companies) DW_Finance Transactions MasterFinance table population 6 hours 21min 6 hours 1 hour Suspicious words Reports 3hr21min Mai 2013 26 SQL Server 2012 Parallel Data Warehouse

27. 2012 © Trivadis Reference Case: Audit Process with PDW DB_ GSAPOP DB_ MasterTables DB_ ReportTables FinanceCube Once data is extracted from SAP: Creating 5 CM Reports & FSCP Finance Cube; Time taken: 30 Minutes DW_Finance Transactions MasterFinance table population 8m50sec load from FlatFile 23min 10m10sec 11 min All 5 Reports within 6min (80) Mai 2013 27 SQL Server 2012 Parallel Data Warehouse

28. 2012 © Trivadis Agenda 1.Positioning Parallel Data Warehouse (PDW) 2.Architecture 3.Working with PDW 4.PDW and Big Data SQL Server 2012 Parallel Data Warehouse Mai 2013 28

29. 2012 © Trivadis Introducing PolyBase Seamless integration of two worlds Single Query; Structured and Unstructured Query and join Hadoop tables with Relational Tables Use Standard SQL language Select, From Where Existing SQL Skillset Save Time and Costs Database HDFS (Hadoop) SQL Server 2012 PDW Powered by PolyBase SQL Analyze All Data Types Mai 2013 29 SQL Server 2012 Parallel Data Warehouse

30. 2012 © Trivadis Additional Resources SQL Server Parallel Data Warehouse (PDW) Landing Page: www.microsoft.com/PDW Introduction to Polybase: http://www.microsoft.com/en-us/sqlserver/solutions-technologies/data- warehousing/polybase.aspx Price/TB comparison: http://www.valueprism.com/resources/resources/Resources/PDW%20Compete %20Pricing%20FINAL.pdf HP QuickSpecs http://h18000.www1.hp.com/products/quickspecs/13830_div/13830_div.html http://h18000.www1.hp.com/products/quickspecs/13830_div/13830_div.pdf Mai 2013 30 SQL Server 2012 Parallel Data Warehouse

31. 2012 © Trivadis Conclusion SQL Server 2012 Parallel Data Warehouse is an MPP based appliance optimized for data warehouse workload  Very Similar to a regular SQL Server, but T-SQL not 100 % identical  Support for development via SQL Server Data Tools  Scalable, balanced platform  Not just storage (data + CPU power)  Central part of Microsoft BI stack  Well integrated in Microsoft management tools SQL Server 2012 Parallel Data Warehouse Mai 2013 31

32. 2012 © Trivadis Visit us, win a price and be prepared for your next adventure SQL Server 2012 Parallel Data Warehouse Mai 2013 32

33. 2012 © Trivadis BASEL BERN LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MÜNCHEN STUTTGART WIEN Let‘s go. Wettbewerb