The document discusses Microsoft's initiatives in big data, including technologies like Hadoop, MapReduce, and HBase, and their application in processing large datasets. It outlines the roles of various big data components and emphasizes Microsoft's partnership with Hortonworks to enhance Hadoop functionality on Windows servers and Azure. Additionally, it mentions how SQL Server integrates with big data tools for better data management and analysis.

1. Microsoft's Big Play for Big Data
Andrew J. Brust
CEO and Founder
Blue Badge Insights
Level: Intermediate

2. Meet Andrew
• CEO and Founder, Blue Badge Insights
• Big Data blogger for ZDNet
• Microsoft Regional Director, MVP
• Co-chair VSLive! and 17 years as a speaker
• Founder, Microsoft BI User Group of NYC
– http://www.msbinyc.com
• Co-moderator, NYC .NET Developers Group
– http://www.nycdotnetdev.com
• “Redmond Review” columnist for
Visual Studio Magazine and Redmond Developer
News
• brustblog.com, Twitter: @andrewbrust

3. My New Blog (bit.ly/bigondata)

4. Read All About It!

5. What is Big Data?
• 100s of TB into PB and higher
• Involving data from: financial data,
sensors, web logs, social media, etc.
• Parallel processing often involved
– Hadoop is emblematic, but other technologies are Big
Data too
• Processing of data sets too large for
transactional databases
– Analyzing interactions, rather than transactions
– The three V’s: Volume, Velocity, Variety
• Big Data tech sometimes imposed on
small data problems

6. What’s MapReduce?
• “Big” input data as key-value pair series
• Partition the data and send to mappers
(nodes in cluster)
• Mappers pre-aggregate by key, then all
output for (a) given key(s) goes to a
reducer
• Reducer completes aggregations; one
output per key, with value
• Map and Reduce code natively written as
Java functions

7. MapReduce, in a Diagram
Input mapper Output
K1
Input mapper Output Input reducer Output
Output
K2
Input mapper Output Input reducer Output
Input
K3
Input mapper Output
Input reducer Output
Input mapper Output
Input mapper Output

8. What’s a Distributed File System?
• One where data gets distributed over
commodity drives on commodity servers
• Data is replicated
• If one box goes down, no data lost
– Except the name node = SPOF!
• BUT: HDFS is immutable
– Files can only be written to once
– So updates require drop + re-write (slow)

9. Hadoop = MapReduce + HDFS
• Modeled after Google MapReduce + GFS
• Have more data? Just add more nodes to
cluster.
– Mappers execute in parallel
– Hardware is commodity
– “Scaling out”
• Use of HDFS means data may well be local
to mapper processing
• So, not just parallel, but minimal data
movement, which avoids network
bottlenecks

10. What’s NoSQL?
• Databases that are non-relational (don’t let
name fool you, some actually use SQL)
• Four kinds:
– Key-Value Store
Schema-free
FYI: Azure Table Storage is an example
– Document Store
All data stored in JSON objects
– Wide-Column Store
Define column families, but not columns
– Graph database
Manage relationships between objects

11. What’s HBase?
• A Wide-Column Store
• Modeled after Google BigTable
• Born at Powerset in 2007
– Powerset acquired by Microsoft in 2008
– Adopted in 2010 by Facebook for messaging platform
• Uses HDFS
– Therefore, Hadoop-compatible
• Hadoop often used with HBase
– But you can use either without the other

12. The Hadoop Stack
• Hadoop
– MapReduce, HDFS
• HBase
– Lesser extent: Cassandra, HyperTable
• Hive, Pig
– SQL-like “data warehouse” system
– Data transformation language
• Sqoop
– Import/export between HDFS, HBase,
Hive and relational data warehouses
• Flume
– Log file integration
• Mahout
– Data Mining

13. What’s Hive?
• Began as Hadoop sub-project
– Now top-level Apache project
• Provides a SQL-like (“HiveQL”)
abstraction over MapReduce
• Has its own HDFS table file format (and it’s
fully schema-bound)
• Can also work over HBase
• Acts as a bridge to many BI products
which expect tabular data

14. Hadoop Distributions
• Cloudera
• Hortonworks
– HCatalog: Hive/Pig/MR Interop
• MapR
– Network File System replaces HDFS
• IBM InfoSphere BigInsights
– HDFS<->DB2 integration
• And now Microsoft…

15. Project “Isotope”
• Work with Hortonworks to create “distro”
of Hadoop that runs on Windows Server
and Windows Azure
– Hortonworks are ex-Yahoo FTEs who are Hadoop
pioneers
• Create ODBC Driver for Hive
– And Excel Add-In that uses it
• Build JavaScript command line and
MapReduce framework
• Contribute it all back to open source
Apache project

16. Hadoop on Azure
• Install onto your own Azure VMs and build
a cluster, or…
• Provision a cluster in one step
– Give it a name
– Choose number of nodes and storage size in cluster
– Wait for it to provision
– Go!

17. Provisioning a Cluster

18. Submitting, Running and
Monitoring Jobs
• Upload a JAR
• Use .NET
• Use the JavaScript Console
• Use the Hive Console

19. Running MapReduce
Jobs

20. Hadoop on Azure Data Sources
• Files in HDFS
• Azure Blob Storage
• Amazon S3 Storage
• Hive Tables

21. Review: ODBC Connection Types
• Registry-based
– User Data Source Name (DSN)
– System DSN
• File-based
– File DSN
• String-based
– DSN-less connection
• We need file-based
• Wizard obfuscates how to do this
• Don’t forget to open the ODBC port!

22. Hive ODBC Setup,
Excel Add-In

23. ODBC Driver’s Untold Story
• Works with any Hive install/Hadoop
cluster, not just Windows-based ones.

24. How Does SQL Server Fit In?
• RDBMS + PDW: Sqoop connectors
• RDBMS: Columnstore Indexes
– Enterprise Edition only
• Analysis Services: Tabular Mode
– Compatible with ODBC Driver
Multidimensional mode is not
• RDBMS + SSAS Tabular: DirectQuery
• PowerPivot (as with SSAS Tabular)
• Power View
– Works against PowerPivot and SSAS Tabular

25. Querying Hadoop from
SQL Server BI

26. The “Data-Refinery” Idea
• Use Hadoop to “on-board” unstructured
data, then extract manageable subsets
• Load the subsets into conventional DW/BI
servers and use familiar analytics tools to
examine
• This is the current rationalization of
Hadoop + BI tools’ coexistence
• Will it stay this way?

27. Usability Impact
• PowerPivot makes analysis much easier,
self-service
• Power View is great for discovery and
visualization; also self-service
• Combine with the Hive ODBC driver and
suddenly Hadoop is accessible to
business users
• Caveats
– Someone has to write the HiveQL
– Can query Big Data, but must have smaller result

28. Other Relevant MS Technologies
• SQL Server Components:
– SQL Server Parallel Data Warehouse
– StreamInsight
• Azure Components:
– Data Explorer
– DataMarket
• Deprecated MSR Project
– Dryad

29. Resources
• Big On Data blog
– http://www.zdnet.com/blog/big-data
• Apache Hadoop home page
– http://hadoop.apache.org/
• Hive & Pig home pages
– http://hive.apache.org/
– http://pig.apache.org/
• Hadoop on Azure home page
– https://www.hadooponazure.com/
• SQL Server 2012 Big Data
– http://bit.ly/sql2012bigdata

30. Thank you
• andrew.brust@bluebadgeinsights.com
• @andrewbrust on twitter
• Want to get the free “Redmond Roundup
Plus?”
– Text “bluebadge” to 22828