Speaker: Karel Coenye Download SQL Server 2012: http://www.microsoft.com/sqlserver/en/us/get-sql-server/try-it.aspx

1. SQL SERVER 2012 AND BIG DATA
Hadoop Connectors for SQL Server

2. TECHNICALLY – WHAT IS HADOOP
• Hadoop consists of two key services:
• Data storage using the Hadoop Distributed File System (HDFS)
• High-performance parallel data processing using a technique called
MapReduce.

3. HADOOP IS AN ENTIRE ECOSYSTEM
• Hbase as database
• Hive as a Data Warehouse
• Pig as the query language
• Built on top of Hadoop and the Map-Reduce framework.

4. HDFS
• HDFS is designed to scale seamlessly
• That‟s it‟s strength!
• Scaling horizontally is non-trivial in most cases.
• HDFS scales by throwing more hardware at it.
• A lot of it!
• HDFS is asynchronous
• Is what links Hadoop to Cloud computing.

5. DIFFERENCES
• SQL Server & Windows 2008 R2′s NTFS?
• Data is not stored in the traditional table column format.
• HDFS supports only forward only parsing
• Databases built on HDFS don‟t guarantee ACID properties
• Taking code to the data
• SQL Server scales better vertically

6. UNSTRUCTURED DATA
• Doesn‟t know/care about column names, column data types, column
sizes or even number of columns.
• Data is stored in delimited flat files
• You‟re on your own with respect to data cleansing
• Data input in Hadoop is as simple as loading your data file into HDFS
• It‟s very close to copying files on an OS.

7. NO SQL, NO TABLES, NO COLUMNS
NO DATA?
• Write code to do Map-Reduce
• You have to write code to get data
• The best way to get data
• write code that calls the MapReduce framework to slices and dices the stored
data
• Step 1 is Map and Step 2 is Reduce.

8. MAP (REDUCE)
• Mapping
• Pick your selection of keys from record (Linefeed)
• Tell the framework what your Key is and what values that key will hold
• MR will deal with actual creation of the Map
• Control on what keys to include or what values to filter out
• End up with a giant hashtable

9. (MAP) REDUCE
• Reducing Data: Once the map phase is complete code moves on to
the reduce phase. The reduce phase works on mapped data and can
potentially do all the aggregation and summation activities.
• Finally you get a blob of the mapped and reduced data.

10. JAVA… VS. PIG…
• Pig is a querying engine
• Has a „business-friendly‟ syntax
• Spits out MapReduce code
• syntax for Pig is called : Pig Latin (Don‟t ask)
• Pig Latin is very similar syntactically to LINQ.
• Pig converts into MapReduce and sends it off to Hadoop then
retrieves the results
• Half the performance
• 10 times faster to write

11. HBASE
• HBase is a key value store on top of HDFS
• This is the NOSql Database
• Very thin layer over raw HDFS
• Data is grouped in a Table that has rows of data.
• Each row can have multiple „Column Families‟
• Each „Column Family‟ contain(s) multiple columns.
• Each column name is the key and it has it‟s corresponding column value.
• Each row doesn‟t need to have the same number of columns

12. HIVE
• Hive is a little closer to RDBMS systems
• Is a DWH system on top of HDFS and Hbase
• Performs join operations between HBase tables
• Maintains a meta layer
• data summation, ad-hoc queries and analysis of large data stores in HFDS
• High level language
• Hive Query Language, looks like SQL but restricted
• No, Updates or Deletes are allowed
• partitioning can be used to update information
o Essentially re-writing a chunk of data.

13. WINDOWS HADOOP- PROJECT ISOTOPE
• 2 Flavours
• Cloud
o Azure CTP
• On Permise
o integration of the Hadoop File System with Active Directory
o integrate System Center Operations Manager with Hadoop
o BI Integration
• Are not all that interesting in and of themselves, but data and tools are
o Sqoop
– Integration with SQL Server
o Flume
– Access to Lots of data

15. SQOOP
• Is a framework that facilitates transfer between (RDBMS) and HDFS.
• Uses MapReduce programs to import and export data;
• Imports and exports are performed in parallel with fault tolerance.
• Source / Target files being used by Sqoop can be:
• delimited text files
• binary SequenceFiles containing serialized record data.

16. SQL SERVER – HORTONWORKS - HADOOP
• Spin-off from Yahoo
• Bridge the technological gaps between Hadoop and Windows Server
• CTP of the Hadoop-based distribution for Windows Server
( somewhere in 2012)
• Will work with Microsoft‟s business-intelligence tools
• including
o Excel
o PowerPivot
o PowerView

17. HADOOP CONNECTORS
• SQL Server versions
• Azure
• PDW
• SQL 2012
• SQL 2008 R2
http://www.microsoft.com/download/en/details.aspx?id=27584

18. WITH SQL SERVER-HADOOP CONNECTOR, YOU CAN:
• Sqoop-based connector
• Import
• tables in SQL Server to delimited text files on HDFS
• tables in SQL Server to SequenceFiles files on HDFS
• tables in SQL Server to tables in Hive
• Result of queries executed on SQL Server to delimited text files on HDFS
• Result of queries executed on SQL Server to SequenceFiles files on HDFS
• Result of queries executed on SQL Server to tables in Hive
• Export
• Delimited text files on HDFS to SQL Server
• DequenceFiles on HDFS to SQL Server
• Hive Tables to tables in SQL Server

19. SQL SERVER 2012 ALONGSIDE THE ELEPHANT
• PowerView utilizes its own class of apps, if you will, that Microsoft is
calling insights.
• SQL Server will extend insights to Hadoop data sets
• Interesting insights can be
• Brought into a SQL Server environment using connectors
• Drive analysis across it using BI tools.

20. WHY USE HADOOP WITH SQL SERVER
• Don‟t just think about big data being large volumes
• Analyze both structured and unstructured datasets
• Think about workload, growth, accessibility and even location
• Can the amount of data stored every day reliably written to a traditional HDD
• Mapreduce is more complex then TSQL
• Many companies try to avoid writing java for queries
• Front ends are immature relative to the tooling available in the relational
database world
• It‟s not going to replace your database, but your database isn‟t likely to replace
Hadoop either.

21. MICROSOFT AND HADOOP
• Broader access of Hadoop to:
• End users
• IT professionals
• Developers
• Enterprise ready Hadoop distribution with greater security,
performance, ease of management.
• Breakthrough insights through the use of familiar tools such as Excel,
PowerPivot, SQL Server Analysis Services and Reporting Services.

22. ENTERPRISE HADOOP
• Installation wizard (IsotopeClusterDeployment)
• Healtcheck and monitoring pages
• Interactive Javascript Console

23. MICROSOFT ENTERPRISE HADOOP
• Machines in the Hadoop cluster must be running Windows Server 2008
or higher
• Ipv4 network enabled on all nodes
• Deployment does not work on Ipv6 only network.
• The ability to create a new user account called “Isotope”.
• Will be created on all nodes of the cluster.
• Used for running Hadoop daemons and running jobs.
• Must be able to copy and install the deployment binaries to each machine
• Windows File Sharing services must be enabled on each machine that will
be joined to the Hadoop cluster.
• .Net Framework 4 installed on all nodes.
• Minimum of 10G free space in C drive (JBOD HDFS configuration is
supported)

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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
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