Modern Data Warehousing
Insights on Any Data of Any Size
James Serra, MicrosoftPDW Technology Solution [email protected]
About Me Business Intelligence Consultant, in IT for 28 years Microsoft, PDW Technology Solution Professional (TSP) Owner of Serra Consulting Services, specializing in end-to-
end Business Intelligence and Data Warehouse solutions using the Microsoft BI stack
Worked as desktop/web/database developer, DBA, BI and DW architect and developer, MDM architect, PDW developer
Been perm, contractor, consultant, business owner Presenter at PASS Business Analytics Conference and PASS
Summit MCSE for SQL Server 2012: Data Platform and BI SME for SQL Server 2012 certs Contributing writer for SQL Server Pro magazine Blog at JamesSerra.com SQL Server MVP Author of book “Reporting with Microsoft SQL Server 2012”
Agenda• Traditional data warehouse & modern data warehouse• APS architecture• Hadoop & PolyBase• Performance and scale• Appliance benefits• Summarize/questions
The traditional data warehouse
4
… 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
Will your current solution handle future needs?
The traditional data warehouse
5
Data sources
OLTP ERP CRM LOB
ETL
Data warehouse
BI and analytics
Increasing data volumes
1
Real-time performance
2
Non-Relational Data
Devices
Web Sensors
Social
New data sources & types
3
Cloud-born data
4
INFRASTRUCTURE
DATA MANAGEMENT & PROCESSING
DATA ENRICHMENT AND FEDERATED QUERY
BI & ANALYTICS
Self-service CollaborationCorporate PredictiveMobile
Extract, transform, load
Single 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
The modern data warehouse
Keep legacy investment
Buy new tier one hardware appliance
Acquire big data solution (Hadoop)
Acquire business intelligence solution
Roadblocks to evolving to a modern data warehouse
Limited
scalability & ability
to handle new data
types
Significant
training & still
siloed
High acquisition/
migration
costs & no
Hadoop
Complex with
low adoption
Solution and issue with that solution
Introducing the Microsoft Analytics Platform SystemYour turnkey modern data warehouse appliance
Next-generation performance at scale
Enterprise-ready big data
Engineered foroptimal value
• Relational and non-relational data in a single appliance
• Enterprise-ready Hadoop
• Integrated querying across Hadoop and APS using T-SQL
• Direct integration with Microsoft BI tools such as Power BI
• Near real-time performance with In-Memory
• Scale-out to accommodate your growing data
• Remove DW bottlenecks with MPP SQL Server
• Concurrency that fuels rapid adoption
• Industry’s lowest DW price/TB
• Value through a single appliance solution
• Value with flexible hardware options using commodity hardware
• Free up space on SAN
Hardware and software engineered togetherThe ease of an appliance
Co-engineered with HP, Dell, and Quanta best practices
Leading performance with commodity hardware
Pre-configured, built, and tuned software and hardware
Integrated support plan with a single Microsoft contact
PDW
HDInsight
PolyBase
Social and web analytics
Live data feeds
Advanced analytics
APS Architecture
Microsoft Analytics Platform System (APS), formally called by its code name “Project Madison”, was released in December 2010 (version 1). PDW is Microsoft’s reworking of the DatAllegro Inc. massive parallel processing (MPP) product started in 2003 and that Microsoft acquired in September 2008. Version 2 of PDW was made available in March, 2013. It was renamed from SQL Server Parallel Data Warehouse (PDW) to Analytics Platform System (APS) in April 2014 (it still includes the PDW region as well as a new HDInsights/Hadoop region).
Polybase was introduced with version 2 of PDW and has new features in PDW v2 AU1 (April 2014).
Case studies: http://www.microsoft.com/casestudies/Case_Study_Search_Results.aspx?Type=1&Keywords=%22Parallel%20Data%20Warehouse%22&LangID=46
APS Logical Architecture (overview)
“Compute” nodeBalanced storage
SQL
“Compute” nodeBalanced storage
SQL
“Compute” nodeBalanced storage
SQL
“Compute” nodeBalanced storage
SQL
DMS
DMS
DMS
DMS
Compute Node – the “worker bee” of APS• Runs SQL Server 2012 APS• Contains a “slice” of each database
Control Node – the “brains” of the APS• Also runs SQL Server 2012 APS• Holds a “shell” copy of each database
• Metadata, statistics, etc• The “public face” of the appliance
Data Movement Services (DMS)• Part of the “secret sauce” of APS• Moves data around as needed• Enables parallel operations among the
compute nodes (queries, loads, etc)
“Control” nodeSQL
DMS
APS Logical Architecture (querying)
“Compute” nodeBalanced storage
SQL“Control” nodeSQL
“Compute” nodeBalanced storage
SQL
“Compute” nodeBalanced storage
SQL
“Compute” nodeBalanced storage
SQL
DMS
DMS
DMS
DMS
DMS
1) User connects to the appliance (control node) and submits query
2) Control node query processor determines best *parallel* query plan
3) APS distributes sub-queries to each compute node
4) Each compute node executes query on its subset of data
5) Each compute node returns a subset of the response to the control node
6) If necessary, control node does any final aggregation/computation
7) Control node returns results to user
APS Data Layout Options“Compute” node Balanced
storageSQL
Balanced storage
Balanced storage
Balanced storage
“Compute” nodeSQL
“Compute” nodeSQL
“Compute” nodeSQL
DMS
DMS
DMS
DMS
Time DimDate Dim IDCalendar YearCalendar QtrCalendar MoCalendar Day
Store Dim
Store Dim IDStore NameStore MgrStore Size
Product Dim
Prod Dim IDProd CategoryProd Sub CatProd Desc
Customer Dim
Cust Dim IDCust NameCust AddrCust PhoneCust Email
Sales Fact
Date Dim IDStore Dim IDProd Dim IDCust Dim IDQty SoldDollars Sold
TD
PD
SD
CD
TD
PD
SD
CD
TD
PD
SD
CD
TD
PD
SD
CD
Sale
s Fa
ct
Replicated
Table copied to each compute node
Distributed
Table spread across compute nodes based on “hash”
Star Schema
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
FactSales_A
FactSales_B
FactSales_C
FactSales_D
FactSales_E
FactSales_F
FactSales_G
FactSales_H
DATA DISTRIBUTION CREATE TABLE FactSales
(ProductKey INT NOT NULL ,OrderDateKey INT NOT NULL ,DueDateKey INT NOT NULL ,ShipDateKey INT NOT NULL ,ResellerKey INT NOT NULL ,EmployeeKey INT NOT NULL ,PromotionKey INT NOT NULL ,CurrencyKey INT NOT NULL ,SalesTerritoryKey INT NOT NULL ,SalesOrderNumber VARCHAR(20) NOT NULL,
) WITH (
DISTRIBUTION = HASH(ProductKey),
CLUSTERED INDEX(OrderDateKey) ,
PARTITION(OrderDateKey RANGE RIGHT FOR VALUES
( 20010601, 20010901,
) ) );
Control Node
…Compute Node 1
Compute Node 2
Compute Node X
Send Create Table SQL to each compute nodeCreate Table FactSales_ACreate Table FactSales_BCreate Table FactSales_C……Create Table FactSales_H
FactSalesA
FactSalesB
FactSalesC
FactSalesD
FactSalesE
FactSalesF
FactSalesG
FactSalesH
FactSalesA
FactSalesB
FactSalesC
FactSalesD
FactSalesE
FactSalesF
FactSalesG
FactSalesH
FactSalesA
FactSale B
FactSalesC
FactSalesD
FactSalesE
FactSalesF
FactSalesG
FactSalesH
Create table metadata on Control Node
APS – Balanced across servers and within
15
Largest Table 600,000,000,000
Randomly distributed across 40 compute nodes (5 racks)
15,000,000,000
In each server randomly distributed to 8 tables 1,875,000,000
Each partition – 2 years data partitioned by week 18,028,846
As an end user or DBA you think about 1 table: LineItem.You run “select * from LineItem”
APS is an appliance, simple to use!You don’t care or need to know that there are actually 320 tables representing your 1 logical table.
InfinibandInfinibandEthernetEthernet
Control NodeFailover Node
Microsoft Storage Spaces 1
Compute Node 1Compute Node 2
Microsoft Storage Spaces 2
Compute Node 3Compute Node 4
Microsoft Storage Spaces 3
Compute Node 5Compute Node 6
Microsoft Storage Spaces 4
Compute Node 7Compute Node 8
CustomerUse
Base Unit (6U):• Redundant Infiniband• Redundant Ethernet• Mgmt & Control (Active)• Rack Failover Node (Passive)
Base Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
Scale Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
¼ R
ack
15
TB
(R
aw
)
1/2
Rack
30
TB
(Raw
)
Customer Space (8U)• ETL Servers• Backup Servers• Passive Unit (Additional spares)
Scale Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
Scale Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
Full R
ack
60
TB
(Raw
)
InfinibandInfinibandEthernetEthernet
Failover Node
Microsoft Storage Spaces 5
Compute Node 9Compute Node 10
Microsoft Storage Spaces 6
Compute Node 11Compute Node 12
Microsoft Storage Spaces 7
Compute Node 13Compute Node 14
Microsoft Storage Spaces 8
Compute Node 15Compute Node 16
CustomerUse
Extension Base Unit (5U):• Redundant Infiniband• Redundant Ethernet• Rack Failover Node (Passive)
Extension Base Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
Scale Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
1¼
R
ack
75
.5TB
(R
aw
)
Customer Space (9U)• ETL Servers• Backup Servers• Passive Unit (Additional spares)
Scale Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
Scale Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
3 R
ack
18
1.2
TB
(Un
com
pre
ssed)
1 1
/2 R
ack
90
.6TB
(Raw
)2 R
ack
12
0.8
TB
(Raw
)
InfinibandInfinibandEthernetEthernet
Failover Node
Microsoft Storage Spaces 9
Compute Node 17Compute Node 18
Microsoft Storage Spaces 10
Compute Node 19Compute Node 20
Microsoft Storage Spaces 11
Compute Node 21Compute Node 22
Microsoft Storage Spaces 12
Compute Node 23Compute Node 24
CustomerUse
Extension Base Unit (5U):• Redundant Infiniband• Redundant Ethernet• Rack Failover Node (Passive)
Extension Base Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
Scale Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
Customer Space (9U)• ETL Servers• Backup Servers• Passive Unit (Additional spares)
Scale Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
Scale Unit (7U):• 2 HP 1U Servers
• (16 Cores/Ea. Total: 32)• Microsoft Storage Spaces 5U
• 1TB Drives• User Data Capacity: 75TB
HP Configuration
• 2 – 56 compute nodes (32-896 cores)
• 1 – 7 racks
• 1, 2, or 3 TB drives
• 15TB – 1.2PB uncompressed
• 75TB – 6PB User data (5:1)
• Up to 7 spare nodes available across the entire appliance
• Dual Infiband: 56Gbps
Details
Next-generation performance at scale
Enterprise-ready big data
Engineered foroptimal value
Microsoft Analytics Platform SystemYour turnkey modern data warehouse appliance
Megabytes
What is big data and why is it valuable to the business A evolution in the nature and use of data in the enterprise
Data complexity: variety and velocity
Peta
byte
s/Volu
me
Historical analysis
Insight analysis
Predictive analytics
Predictive forecasting
Valu
e t
o t
he b
usi
ness
What is Hadoop?
Microsoft Confidential
19
Distributed, scalable system on commodity HW
Composed of a few parts:
HDFS – Distributed file system
MapReduce – Programming model
Others: HBase, R, Pig, Hive, Flume, Mahout, Avro, Zookeeper
Core Services
OPERATIONAL SERVICES
DATASERVICES
HDFS
SQOOP
FLUME
NFS
LOAD & EXTRACT
WebHDFS
OOZIE
AMBARI
YARN
MAP REDUCE
HIVE &HCATALOGPIG
HBASEFALCON
Hadoop Cluster
compute&
storage . . .
. . .
. .compute
&storage
.
.
Hadoop clusters provide scale-out storage and distributed data processing on commodity hardware
Move HDFS into the warehouse before analysis
HDFS (Hadoop)
ETL
WarehouseHDFS (Hadoop)
Learn new skills
TSQL
Build Integrate ManageMaintainSupport
Complex query and analysis with big data todaySteep learning curve, slow and inefficient
Hadoop ecosystem
“New” data sources
Devices
Web Sensor Social
“New” data sources“New” data sources
Devices
Web Sensor Social
APS delivers enterprise-ready Hadoop with HDInsightManageable, secured and highly available Hadoop integrated into the appliance
High performance tuned within the appliance
End-user authentication with Active Directory
Accessible insights for everyone with Microsoft BI tools
Managed and monitored using System Center
100% Apache Hadoop
SQL ServerParallel DataWarehouse
Microsoft HDInsight
PolyBase
Leverage your existing TSQL skills
Parallel Data Warehouse workload
HDInsight workload
Fabric
Hardware
Applia
nce
A region is a logical container within an appliance
Each workload contains the following boundaries:• Security
• Metering
• Servicing
APS appliance overview
Select… Result set Provides a single T-SQL query model (“semantic layer”) for APS and Hadoop with rich features of T-SQL, including joins without ETL
Uses the power of MPP to enhance query execution performance
Supports Windows Azure HDInsight to enable new hybrid cloud scenarios
Provides the ability to query non-Microsoft Hadoop distributions, such as Hortonworks and Cloudera
Use existing SQL skillset, no IT intervention
Query Hadoop data with T-SQL using PolyBaseBringing the worlds or big data and the data warehouse together for users and IT
SQL ServerParallel DataWarehouse
Cloudera CHD Linux 4.3
Hortonworks HDP 2.0 (Windows, Linux)
Windows AzureHDInsight
PolyBase
Microsoft HDInsightHDP 1.3
Query re la t i ona l + non re la t i ona l
Others? Federated querying
AU1: Windows Azure storage blob (WASB)
Use cases where PolyBase simplifies using Hadoop dataBringing islands of Hadoop data together
High performance queries against Hadoop data(Predicate pushdown)
Archiving data warehouse data to Hadoop (move)(Hadoop as cold storage)
Exporting relational data to Hadoop (copy)(Hadoop as backup/DR, analysis,
cloud use)Importing Hadoop data into data warehouse (copy)
(Hadoop as staging area)
Big data insights for anyoneNative Microsoft BI integration to create new insights with familiar tools
Tools like Power BI minimize ITintervention for discovering dataT-SQL for DBA and power users to join relational and Hadoop data
Hadoop tools like map-reduce, Hive and Pig for data scientists
Leverages high adoptionof Excel, Power View, Power Pivot, and SSAS
Power Users
Data Scientist
Everyone else using Microsoft BI tools
Next-generation performance at scale
Enterprise-ready big data
Engineered foroptimal value
Microsoft Analytics Platform SystemYour turnkey modern data warehouse appliance
Performance limitations and scale with a traditional data warehouse
Diminishing scale as requirements grow
Scale up Rowstore
Sub-optimal performance for many data warehouse queries
Data
Page 1 Page 2 Page 3
Querying data by row
C1 C2 C3 C4
R1 R1 R1 R1
R2 R2 R2 R2
R3 R3 R3 R3
R4 R4 R4 R4
R5 R5 R5 R5
R6 R6 R6 R6
Forklift
Forklift
Scale-out Massively Parallel Processing (MPP) parallelizes queries (speed-driven not just capacity-driven)
Multiple nodes with dedicated CPU, memory, storage “shared-nothing”
Incrementally add HW for near-linear scale to multi-PB (no need to delete older data, stage)
Handles query complexity and concurrency at scale
No “forklift” of prior warehouse to increase capacity
Start small with a few terabyte warehouse
Query while you load (250GB/hour per node)
Scaling out relational data to petabytesScale-out technologies in the Analytics Platform System
28
PDW
0TB 6PB
PDW/ HDInsight
PDW/ HDInsight
PDW/ HDInsight
PDW/ HDInsight
PDW/ HDInsight
PDW/ HDInsight
Blazing fast performanceMPP and In-memory columnstore for next-generation performance
• Store data in columnar format for massive compression
• Load data into or out of memory for next-generation performance
• Updateable and clustered for real-time trickle loading
• No secondary indexes required
29
Up to 100x faster queries
Updatable clustered columnstore vs. table with customary indexing
Up to 15xmore compression
Columnstore index representation
C1
C3
C5
C4
C2
C6
Parallel query execution
Query
Results
PDW MPP vs. SQL Server SMP
• 2B row fact sales table
• Nine different queries including
• simple counts• Sum/min/max with
group-bys• Multiple inner joins
with 3-5 dimension tables
• Multiple sub-queries across the big fact table
BI Tools
Reporting and cubes
SQL Server SMP
Concurrency that fuels rapid adoptionGreat performance with mixed workloads
Analytics Platform System
ETL/ELT with SSIS, DQS, MDS
ERP CRM LOB APPS
ETL/ELT with DWLoader
Hadoop / Big Data
PDW
HDInsight
PolyBase
Ad hoc queries
Intra-Day
Near real-time
Fast ad hoc
Columnstore
Polybase
CRTAS
Link Table
Real-Time
ROLAP / MOLAP DirectQuery
SNAC
Next-generation performance at scale
Enterprise-ready big data
Engineered foroptimal value
Microsoft Analytics Platform SystemYour turnkey modern data warehouse appliance
APS provides the industry’s lowest DW appliance price/TBReshaped hardware specs through software innovation Price per terabyte for leading vendors Significantly lower
price per TB than the closest competitor
Price per TB User-Available Storage (Compressed)
NOTE: Orange line indicates average price per TB.
Thou
sands
Oracle EMC IBM Teradata Microsoft
$30
$25
$20
$15
$10
$5
$0
Lower storage costs with Windows Server 2012 Storage Spaces
Small cost gap between multiple clustered HP DL980's with SAN vs APS 1/4 rack
Virtualized architecture overview
Host 2
Host 1
Host 3
Host 4
Economical disk
storageIB andEthernet
Direct attached SAS
Base UnitCTL
MAD
AD
VMM
Compute 2
Compute 1
• APS engine• DMS Manager• SQL Server 2012 Enterprise Edition (APS build)
Software details
• All hosts run Windows Server 2012 Standard and Windows Azure Virtual Machines
• Fabric or workload in Hyper-V Virtual Machines
• Fabric virtual machine, management server (MAD01), and control server (CTL) share one server
• APS agent that runs on all hosts and all virtual machines
• DWConfig and Admin Console
• Windows Storage Spaces and Azure Storage blobs
• Does not require expertise in Hyper-V or Windows
APS High-Availability
X XCompute Host 1
Compute Host 2
XControl Host
Failover Host
Infin
iban
d 1
Ethe
rnet
1
Infin
iban
d 2
Ethe
rnet
2
XXXFAB AD VMM MAD CTL
Compute 2 VM
Compute 1 VMCompute 1 VMIn
finib
and
1
Ethe
rnet
1
• No Single Point-Of-Failure• No need for SQL Server
Clustering
Less DBA Maintenance/Monitoring• No index creation• No deleting/archiving data to save space• Management simplicity (System Center)• No blocking• No logs• No query hints• No wait states• No IO tuning• No query optimization/tuning• No index reorgs/rebuilds• No managing filegroups• No shrinking/expanding databases• No managing physical servers• No patching servers and software
RESULT: DBA spend more of their time as architects and not baby sitters!
The no-compromise modern data warehouse solution Microsoft’s turn-key modern data warehouse appliance Analytics Platform System
Microsoft
• Improved query performance
• Faster data loading• Improved concurrency• Less DBA maintenance• Limited training needed• Use familiar BI tools• Ease of appliance
deployment• Mixed workload
support
• Improved data compression
• Scalability• High availability• PolyBase• Integration with cloud-
born data• HDInsight/Hadoop
integration• Data warehouse
consolidation• Easy support model
Summary of Benefits
Bold = benefits of APS over upgrading to SQL Server 2014
© 2013 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.
Questions?
James Serra, MicrosoftPDW Technology Solution ProfessionalEmail me at: [email protected] me at: @JamesSerra Link to me at: www.linkedin.com/in/JamesSerra Visit my blog at: JamesSerra.com
Blog about PDW topics: http://www.jamesserra.com/archive/category/pdw/