Data Warehouse in the Cloud Marketing or Reality? Alexei Khalyako Sr. Program Manager Windows Azure Customer Advisory Team

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2 Data Warehouse in the Cloud Marketing or Reality? Alexei Khalyako Sr. Program Manager Windows Azure Customer Advisory Team

3 Data Warehouse we used to know High-End workload High-End hardware Special know-how

4 Thousands of departmental level DW Relatively low perf SLA Reality is *BeyeNetwork Big Data research

5 New BI demands *BeyeNetwork Big Data research Utilize external data sources Non Structured Data Origin is in the Cloud

6 New opportunity Platform is there Iaas SQL VM Paas SQL Azure DB Closer to data Less administrative overhead Lower initial and TCO cost

7 SQL Server Data Warehousing in Windows Azure Virtual Machines Inspired by the Fast Track Reference Architecture guide Based on the High Memory images Up to 1TB MSDN: SQL Server Data Warehousing in Windows Azure Virtual Machines

8 High Memory VM in Azure

9 How to deploy Powershell script Windows Azure Gallery

10 The Azure Data Warehouse under the hood

11 Data Warehouse Lifecycle Thoughts on the architecture Creating DB Connectivity Populating Database Initial data loading OR Backup/Restore Incremental data loading Compression Query performance

12 Thoughts on the architecture Data Loading Minimize Log impact Scale loading streams Do not invent the wheel and follow the Data loading Performance guide Query Performance! Do not invent the wheel and follow the Data loading Performance guide

13 Windows Azure VM Architecture Disks implemented as a shared multi-tenant service Built-in triple redundancy, optional geo-redundancy Performance less predictable than on-prem Host machines, storage services, network bandwidth shared between subscribers Perf can depend on where and when VM is provisioned Subject to maintenance operations Granular control & configurability vs. cost, simplicity, out of box redundancy LB Front-ends Partition Layer Stream Layer Intra-stamp replication Storage Stamp Storage Location Service Geo-replication LB Front-ends Partition Layer Stream Layer Intra-stamp replication Storage Stamp

14 Log Primary Tweaks to improve IO Subsystem Database file initialization GPEdit.msc Data file placement SQL Striping for User Data and TempDB Aggregated throughput Set the size and data grow options wisely *You may do it differently. Then Create 350GB DB took ~3 hours

15 Scaling IO Options Windows Storage Spaces Log drive Not clear support story SQL Data Files Spread File Group over all drives

16 Scaling IO Options Data disk (read) LOG (write) SQLIO Single Data Disk (256K) SQLIO Windows Storage Spaces X3 Disks (256K) SQLIO SQL Striping x3 Disk (256K) SQLIO Single Data Disk (64K) SQLIO Windows Storage Spaces X3 Disks (64K) SQLIO SQL Striping x3 Disk (64K) * CUMULATIVE DATA: throughput metrics: IOs/sec: 288 MBs/sec: CUMULATIVE DATA: throughput metrics: IOs/sec: MBs/sec: CUMULATIVE DATA: throughput metrics: IOs/sec: MBs/sec: CUMULATIVE DATA: throughput metrics: IOs/sec: MBs/sec: CUMULATIVE DATA: throughput metrics: IOs/sec: MBs/sec: CUMULATIVE DATA: throughput metrics: IOs/sec: MBs/sec: * But we can access one file at the time!

17 Connectivity Options Windows Azure VM End Points Point-to-Site /Site-to-Site *Other options are also available ( FTP)

18 What and how we tested

19 Copy backup to the Data Disks Backup/Restore to/from URL ETL to the new DB Getting initial data

20 Backup to the Local Data Disk URL is fast! Backup to the URL DB Size Time Speed 244GB 3 hours 22,978 MB/sec DB Size Time Speed 244GB 46 min 90,667 MB/sec

21 DB and Data Loading? Data loading? Tools (BCP, SSIS..)? Time SLA? Query Performance? Indexing strategy? Sizing? Compression

22 Loading Data in Azure Smaller batches (10K -15K rows) Retry logic Network latency is high Parallel loading!! Start with: SSIS for Hybrid Data Movement SSIS Performance and Operational guide

23 Baseline Understand Data Sources performance Flat File in Azure VM ~60 MB/sec /reads SQLIO shows the max throughput of the IO subsystem on the DB side App performance can be different

24 Parallel Loading Flat file Max 60 MB/sec Flat file Max 60 MB/sec Mod(7) function 8 destinations to keep all CPU busy on the DW site

25 Begin to load

26 Monitoring Loading Performance You will be followed by TOP waits: ASYNC_NETWORK_IO PAGEIOLATCH_EX WRITELOG PAGEIOLATCH_UP SOS_SCHEDULER_YIELD PAGEIOLATCH_SH PAGELATCH_UP PREEMPTIVE_OLEDBOPS Network IO Disk IO CPU

27 Loading: table options Heap rows Elapsed time: 01:06: Heap compressed rows Elapsed time: 05:12:06.094

28 Loading: table options HEAP rows Elapsed time: 01:06: Clustered Index Sort! Elapsed time: 01:20:12.547

29 Query Performance Heap Primary Key/Clustered Index Compression

30 Query Execution time Query performance: results Heap Clustered Index Clustered Index Compressed 0.00 Query Type

31 Please welcome on stage SQL 2014

32 Data files to BLOBs Updateable Clustered Column Store index What s new?

33 Loading data Heap 1 hour 1 min Clustered Column store Index Load test 2 hours 16 min

34 Query Execution time SQL x x39 x55 x CI CIComp CSI Query type CI CIComp CSI

35 Query 19 Estimates vs Actual

36 And the winner is SQL Server 2014!!

37 Summary Easy and fast deployment through he Gallery or PS scripts Azure Data Warehouse is consistent with the most of the best practices Query Loading Low Initial investments and TCO

38 THANK YOU! For attending this session and PASS SQLRally Nordic 2013, Stockholm

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