#mstechdays Business Intelligence
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Business Intelligence
Massivement Parallèle avec PDW !
Lionel PénuchotGilbert Breton
#mstechdays Business Intelligence
… data warehousing has reached the most significant tipping point since its inception. The biggest, possibly most elaborate data management system in IT is changing.
– Gartner, “The State of Data Warehousing in 2012”
Data sources
OLTP ERP CRM LOB
ETL
Data warehouse
BI and analytics
L’approche traditionnelle d’un DWH
#mstechdays Business Intelligence
L’approche traditionnelle d’un DWH
Data sources
OLTP ERP CRM LOB
ETL
Data warehouse
BI and analytics
Increasing data volumes
1
Real-time data
2
Non-Relational Data
Devices Web Sensors Social
New data sources & types
3Cloud-born data
4
#mstechdays Business Intelligence
Modernisation d’un projet décisionnel
INFRASTRUCTURE
DATA MANAGEMENT & PROCESSING
DATA ENRICHMENT AND FEDERATED QUERY
BI & ANALYTICS
Self-service CollaborationCorporate PredictiveMobile
Extract, transform, loadSingle query model Data quality Master data management
Non-relationalRelational Analytical Streaming Internal & External
Data sources
OLTP ERP CRM LOB
Non-Relational Data
Devices Web Sensors Social
#mstechdays Business Intelligence
INFRASTRUCTURE
DATA MANAGEMENT & PROCESSING
DATA ENRICHMENT AND FEDERATED QUERY
BI & ANALYTICSThe Microsoft Data Platform
Self-service CollaborationCorporate PredictiveMobile
Extract, transform, loadSingle query model Data quality Master data management
Non-relationalRelational Analytical Streaming Internal & External
VirtualizationScalability Security and Identity Quality of service
Show All
Close All
#mstechdays Business Intelligence
All Volumes Any DataReal-time Performance
#mstechdays Business Intelligence
All Volumes Any DataReal-time Performance
#mstechdays Business Intelligence
Scale-out données relationnelles
Massively Parallel Processing (MPP) parallelizes queries
Multiple nodes with dedicated CPU, memory, storage
Incrementally add HW for near linear scale to multi-PB
Handles query complexity and concurrency at scale
No fork-lift of prior warehouse to increase capacity
Scale OUT
From Terabytes to Mult i -Petabytes
Scale out technologies in SQL Server Parallel Data Warehouse
Relational
#mstechdays Business Intelligence
Scale-out données non relationnelles
Create Hadoop cluster for your data requirements
Seamlessly add more compute to fit your demand
Scales out linearly Shut down clusters when you are done
Scale Out “Big Data”
Scale OUT
Scale out non-relational data in HDInsight (for Azure or PDW)
Non-relational
#mstechdays Business Intelligence
• Basel III implementation on PDW• RDBMS> Windows HPC computation
farm-> PDW store and aggregate• Run the aggregation daily
Financial Risk Management System Use case
# Contracts x 1 Million
SMP (Extrapolated) hour:min
4 Node PDW
10 Node PDW
Improvement4 node MPP vs SMP
Improvement10 node MPP vs SMP
Scale factor 4 vs 10 nodes
155 min
8 min 18sec
3 min 30sec 6.6x 15.7x 2.4x
302 hr 45
min25 min 50sec
9 min 52sec 6.4x 16.7x 2.6x
109 hr 9min 1 hr 40min
32 min 26sec 5.5x 16.9x 3.1x
2522 hr 52min 4 hr 31min 1 hr 29min
5.1x 15.4x 3.0x
50
1day 21 hr 45min
9 hr 35min3 hr 7 min 4.8x 14.7x 3.1x
démo
#mstechdays Business Intelligence
SCALE-OUT WITH MPP TECHNOLOGIES IN PDW
#mstechdays Business Intelligence
All Volumes Any DataReal-time Performance
#mstechdays Business Intelligence
All Volumes Any DataReal-time Performance
#mstechdays Business Intelligence
Traitement In-Memory
Stores data in columnar format for massive compression
Loads data into or out of memory for next-gen performance
Leverage your existing hardware regardless of specs
Updateable and clustered for real-time trickle loading
Customer
Sales
Country
Supplier
Products
Up to 100x faster and 10x compress ion
In-memory columnstore for next-generation performance
Relational
#mstechdays Business Intelligence
Insight en quasi temps réel
Low latency with sub-zero processing of large event streams
Continuous insight through historical data mining
Management simplicity and flexible deployment
EVENT TARGETS
EVENT SOURCES
Real - t ime Ins ights
Real-time with complex event processing
Relational
#mstechdays Business Intelligence
• 30 users, 6 TB of data (5 years of history)• Daily volumes: 50 million rows • Bank data for customer and fraud analysis
– Weekly analysis of deviance and strange transactions, suspect of money laundry.
• Retail data for customer purchase analysis on receipt line level– For example backtracking last 6 months per customer, per product and per
category; resulting in marketing offers such as “you like item A, other who Item A also purchase item B” etc.
• Now queries cover half a year of data complete in 1.5 minute on PDW compared to 8 minutes covering a month of data on traditional DW.
Retail industry Use case
démo
#mstechdays Business Intelligence
IN-MEMORY COLUMNSTORE FOR NEXT-GEN PERFORMANCE
#mstechdays Business Intelligence
All Volumes Any DataReal-time Performance
#mstechdays Business Intelligence
All Volumes Any DataReal-time Performance
#mstechdays Business Intelligence
Exploration de données non relationnelles
Manage non-relational data
100% Apache-based
Management simplicity of Windows
Bringing Hadoop to software, appliance, cloud
“Big Data” with s impl ic i ty
Windows Azure
Parallel Data Warehouse
Hortonworks Data Platform
Hadoop cluster in HDP for Windows and HDInsight
Non-relational
#mstechdays Business Intelligence
Intégration avec Polybase
Query relational and Hadoop in parallel
Single query
No need to ETL Hadoop data into DW
Query Hadoop with existing T-SQL skillsQuery relat ional + non
relat ional
SQL Result set
Relational data
PolyBase
Integrated query with PolyBase in SQL PDW
démo
#mstechdays Business Intelligence
POLYBASE TO JOIN RELATIONAL AND NON-RELATIONAL DATA
#mstechdays Business Intelligence
Différentes options de déploiement
Box Software
CloudAppliances
SQL Server
Hortonworks Data Platform
Parallel Data Warehouse
SQL Server for data warehousing in Windows Azure
VMs
HDInsight for Windows Azure
SQL Server Fast Track
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
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