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Building Big Data Solutions in the Microsoft Platform
Jesus RodriguezTellago, Inc, Tellago Studios
Big Data?
About Me…• Hackerpreneur• Co-Founder Tellago, Tellago Studios, Inc.• Microsoft Architect Advisor• Microsoft MVP• Oracle ACE• Speaker, Author• http://weblogs.asp.net/gsusx • http://jrodthoughts.com • http://moesion.com
Agenda• Big Data Overview• MS HDInsight
– Map Reduce– HDFS– Hive– Pig – Sqoop
• HDInsight Service• The Hadoop Ecosystem• The Future….
Big Data?
• A bunch of data?• An industry?• An expertise?• A trend?• A cliché?
A Clue?• 2008: Google processes 20 PB a day• 2009: Facebook has 2.5 PB user data
+ 15 TB/day • 2009: eBay has 6.5 PB user data +
50 TB/day• 2011: Yahoo! has 180-200 PB of data• 2012: Facebook ingests 500 TB/day
We Love Data!
But...
Processing Large Amounts of Data is Complicated....
Sucessful Big Data = Scalable Computing + Large Storage
A Trivial Model
Not So Fast....
Parallel Data Computing is Complicated
So Is Large Data Storage
Enter the World of Hadoop...
Hadoop Design Principles• System Shall Manage and Heal
Itself• Performance Shall Scale Linearly • Compute Shall Move to Data• Simple Core, Modular and
Extensible
Hadoop History• 2002-2004: Doug Cutting and Mike Cafarella started working on Nutch• 2003-2004: Google publishes GFS and MapReduce papers • 2004: Cutting adds DFS & MapReduce support to Nutch• 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch• 2007: NY Times converts 4TB of archives over 100 EC2s• 2008: Web-scale deployments at Y!, Facebook, Last.fm• April 2008: Yahoo does fastest sort of a TB, 3.5mins over 910 nodes• May 2009:
– Yahoo does fastest sort of a TB, 62secs over 1460 nodes– Yahoo sorts a PB in 16.25hours over 3658 nodes
• June 2009, Oct 2009: Hadoop Summit, Hadoop World• September 2009: Doug Cutting joins Cloudera
Hadoop Ecosystem
HDFS(Hadoop Distributed File System)
HBase (key-value store)
MapReduce (Job Scheduling/Execution System)
Pig (Data Flow) Hive (SQL)
BI ReportingETL Tools
Avr
o (S
eri
aliz
atio
n)
Zo
oke
ep
r (C
oo
rdin
atio
n)
Sqoop
RDBMS
(Streaming/Pipes APIs)
Microsoft & Hadoop
HDInsight
HDFS
HDFS Is…• A distributed file system• Redundant storage• Designed to reliably store data using
commodity hardware• Designed to expect hardware failures• Intended for large files• Designed for batch inserts• The Hadoop Distributed File System
HDFS at a Glance
Block Size = 64MBReplication Factor = 3
Cost/GB is a few ¢/month vs $/month
HDInsight
HDFS Demo
Map Reduce
Map Reduce Is…• A programming model for expressing
distributed computations at a massive scale
• An execution framework for organizing and performing such computations
• An open-source implementation called Hadoop
Map Reduce At a Glance
HDInsight
Map Reduce Demo
Hive
Hive Is…• A system for managing and querying structured
data built on top of Hadoop– Map-Reduce for execution– HDFS for storage– Metadata on raw files
• Key Building Principles:– SQL as a familiar data warehousing tool– Extensibility – Types, Functions, Formats, Scripts– Scalability and Performance
Hive Architecture
HDInsight
Hacking with Hive
Pig
Pig Is…Apache Pig is a platform for analyzing large data sets that
consists of a high-level language (PigLatin) for expressing data analysis programs, coupled with infrastructure for evaluating these programs.
• Ease of programming
• Optimization opportunities
• Extensibility
• Built upon Hadoop
Pig Architecture
Parser (PigLatinLogicalPlan)
Optimizer (LogicalPlan LogicalPlan)
Compiler (LogicalPlan PhysiclaPlan MapReducePlan)
ExecutionEngine
Pig Context
Hadoop
Grunt (Interactive shell) PigServer (Java API)
HDInsight Rocking Data Processing
with Pig
Sqoop
Sqoop Is…• Easy import of data from many
databases to HDFS• Generates code for use in
MapReduce applications• Integrates with Hive
Sqoop Architecture
HDInsight
Bulk Data Loading Using Sqoop
HDInsight Service
HDInsight Service Architecture
HDInsight
HDInsight Service Overview
Hadoop Considerations
Super Crowded Ecosystem
The Hadoop Ecosystem
Hadoop is not a silver bullet...
Some Challenges• Hadoop doesn’t power big data applications
– Not a transactional datastore. Slosh back and forth via ETL
• Processing latency– Non-incremental, must re-slurp entire dataset every
pass
• Ad-Hoc queries– Bare metal interface, data import
• Graphs– Only a handful of graph problems amenable to MR
Beyond Hadoop• Percolator(incremental processing)http://research.google.com/pubs/pub36726.html • Dremel(ad-hoc analysis queries)http://research.google.com/pubs/pub36632.html • Pregel (Big graphs)http://dl.acm.org/citation.cfm?id=1807184
In the Meantime...
Takeaways • Hadoop provides the foundation of big
data solutions• Computing and storage are the
fundamental components of Hadoop• HDInsight Server and Service are
Microsoft’s distributions of Hadoop• HDInsight is just one component of
Microsoft’s BI strategy
http://www.tellagostudios.com http://jrodthoughts.com
http://twitter.com/#!/jrodthoughtshttp://weblogs.asp.net/gsusx
