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Microsoft's Big Play for Big Data
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
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
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!
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!
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
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