The document discusses the integration of Big Data and SQL Server, covering foundational concepts such as the 3 V's (Volume, Velocity, Variety) and the role of Apache Hadoop in managing Big Data. It emphasizes the distinction between Big Data and traditional data warehousing, highlighting that Big Data primarily involves batch processing for large files while in-memory analytics can be used for real-time insights. It also presents various tools and methodologies for handling data, including MapReduce, Sqoop, and the use of NoSQL databases in a Big Data analytics context.

1. Big Data with SQL Server
Philly Code Camp 2013.1
May 2013
http://www.pssug.org
Mark Kromer
http://www.kromerbigdata.com
@kromerbigdata
@mssqldude
makromer@microsoft.com

2. ‣What is Big Data?
‣The Big Data and Apache Hadoop environment
‣Big Data Analytics
‣SQL Server in the Big Data world
‣Microsoft + Hortonworks (Yahoo!) = HDInsights
What we’ll (try) to cover today
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3. Big Data 101
‣ 3 V’s
‣ Volume – Terabyte records, transactions, tables, files
‣ Velocity – Batch, near-time, real-time (analytics), streams.
‣ Variety – Structures, unstructured, semi-structured, and all the above in a mix
‣ Text Processing
‣ Techniques for processing and analyzing unstructured (and structured) LARGE files
‣ Analytics & Insights
‣ Distributed File System & Programming

4. ‣ Batch Processing
‣ Commodity Hardware
‣ Data Locality, no shared storage
‣ Scales linearly
‣ Great for large text file processing, not so great on small files
‣ Distributed programming paradigm

5. Popular Hadoop Distributions
Hosted PaaS Hadoop platforms: Amazon
EMR, Pivotal, Microsoft Hadoop on Azure

6. ‣ Big Data ≠ NoSQL
‣ NoSQL has similar Internet-scale Web origins of Hadoop stack (Yahoo!,
Google, Facebook, et al) but not the same thing
‣ Facebook, for example, uses Hbase from the Hadoop stack
‣ Big Data ≠ Real Time
‣ Big Data is primarily about batch processing huge files in a distributed manner
and analyzing data that was otherwise too complex to provide value
‣ Use in-memory analytics for real time insights
‣ Big Data ≠ Data Warehouse
‣ I still refer to large multi-TB DWs as “VLDB”
‣ Big Data is about crunching stats in text files for discovery of new patterns and
insights
‣ Use the DW to aggregate and store the summaries of those calculations for
reporting
Mark’s Big Data Myths

7. Big Data Analytics Web Platform - Example

8. using Microsoft.Hadoop.MapReduce;
using System.Text.RegularExpressions;
public class TotalHitsForPageMap : MapperBase
{
public override void Map(string inputLine, MapperContext context)
{
context.Log(inputLine);
var parts = Regex.Split(inputLine, "s+");
if (parts.Length != expected) //only take records with all values
{
return;
}
context.EmitKeyValue(parts[pagePos], hit);
}
}
MapReduce Framework (Map)

9. public class TotalHitsForPageReducerCombiner : ReducerCombinerBase
{
public override void Reduce(string key, IEnumerable<string> values, ReducerCombinerContext
context)
{
context.EmitKeyValue(key, values.Sum(e=>long.Parse(e)).ToString());
}
}
public class TotalHitsJob : HadoopJob<TotalHitsForPageMap,TotalHitsForPageReducerCombiner>
{
public override HadoopJobConfiguration Configure(ExecutorContext context)
{
var retVal = new HadoopJobConfiguration();
retVal.InputPath = Environment.GetEnvironmentVariable("W3C_INPUT");
retVal.OutputFolder = Environment.GetEnvironmentVariable("W3C_OUTPUT");
retVal.DeleteOutputFolder = true;
return retVal;
}
}
MapReduce Framework (Reduce & Job)

10. ‣ Linux shell commands to access data in HDFS
‣ Put file in HDFS: hadoop fs -put sales.csv /import/sales.csv
‣ List files in HDFS:
‣ c:Hadoop>hadoop fs -ls /import
Found 1 items
-rw-r--r-- 1 makromer supergroup 114 2013-05-07 12:11 /import/sales.csv
‣ View file in HDFS:
c:Hadoop>hadoop fs -cat /import/sales.csv
Kromer,123,5,55
Smith,567,1,25
Jones,123,9,99
James,11,12,1
Johnson,456,2,2.5
Singh,456,1,3.25
Yu,123,1,11
‣ Now, we can work on the data with MapReduce, Hive, Pig, etc.
Get Data into Hadoop

11. create external table ext_sales
(
lastname string,
productid int,
quantity int,
sales_amount float
)
row format delimited fields terminated by ',' stored as
textfile location '/user/makromer/hiveext/input';
LOAD DATA INPATH '/user/makromer/import/sales.csv' OVERWRITE
INTO TABLE ext_sales;
Use Hive for Data Schema and Analysis

12. ‣ sqoop import –connect jdbc:sqlserver://localhost –username sqoop -password
password –table customers -m 1
‣ > hadoop fs -cat /user/mark/customers/part-m-00000
‣ > 5,Bob Smith
‣ sqoop export –connect jdbc:sqlserver://localhost –username sqoop -password
password -m 1 –table customers –export-dir /user/mark/data/employees3
‣ 12/11/11 22:19:24 INFO mapreduce.ExportJobBase: Transferred 201 bytes in
32.6364 seconds (6.1588 bytes/sec)
‣ 12/11/11 22:19:24 INFO mapreduce.ExportJobBase: Exported 4 records.
Sqoop
Data transfer to & from Hadoop & SQL Server

13. SQL Server Big Data – Data Loading
Amazon HDFS & EMR Data Loading
Amazon S3 Bucket

14. Role of NoSQL in a Big Data Analytics Solution
‣ Use NoSQL to store data quickly without the overhead of RDBMS
‣ Hbase, Plain Old HDFS, Cassandra, MongoDB, Dynamo, just to name a few
‣ Why NoSQL?
‣ In the world of “Big Data”
‣ “Schema later”
‣ Ignore ACID properties
‣ Drop data into key-value store quick & dirty
‣ Worry about query & read later
‣ Why NOT NoSQL?
‣ In the world of Big Data Analytics, you will need support from analytical tools with a
SQL, SAS, MR interface
‣ SQL Server and NoSQL
‣ Not a natural fit
‣ Use HDFS or your favorite NoSQL database
‣ Consider turning off SQL Server locking mechanisms
‣ Focus on writes, not reads (read uncommitted)

15. ‣ SQL Server Database
‣ SQL 2012 Enterprise Edition
‣ Page Compression
‣ 2012 Columnar Compression on Fact Tables
‣ Clustered Index on all tables
‣ Auto-update Stats Asynch
‣ Partition Fact Tables by month and archive data with sliding window technique
‣ Drop all indexes before nightly ETL load jobs
‣ Rebuild all indexes when ETL completes
‣ SQL Server Analysis Services
‣ SSAS 2012 Enterprise Edition
‣ 2008 R2 OLAP cubes partition-aligned with DW
‣ 2012 cubes in-memory tabular cubes
‣ All access through MSMDPUMP or SharePoint
SQL Server Big Data Environment

16. ‣Columnstore
‣Sqoop adapter
‣PolyBase
‣Hive
‣In-memory analytics
‣Scale-out MPP
SQL Server Big Data Analytics Features

17. 17 17
Sensors Devices Bots Crawlers
ERP CRM LOB APPs
Unstructured and Structured Data
Parallel Data Warehouse
Hadoop On
Windows
Azure
Hadoop On
Windows
Server
Connectors
S
S
R
S
SSAS
BI Platform
Familiar End User Tools
Excel with PowerPivot Embedded BIPredictive Analytics
Data Market Place
Data Market
Petabytes of Data
(Unstructured)
Hundreds of TB of Data
(structured)
Microsoft’s Data Solution – Big Data & PDW

18. MICROSOFT BIG DATA
Discover Combine Refine
Relational Non-relational Streaming
immersive data
experiences
connecting with
worlds data
any data, any
size, anywhere
Self-Service Collaboration Corporate Apps Devices
Analytical
Parallel Data Warehouse
Microsoft HDInsight Server
HDInsight Service
StreamInsight
PowerPivot
Power View

20. Microsoft .NET Hadoop APIs
‣ WebHDFS
‣ Linq to Hive
‣ MapReduce
‣ C#
‣ Java
‣ Hive
‣ Pig
‣ http://hadoopsdk.codeplex.com/
‣ SQL on Hadoop
‣ Cloudera Impala
‣ Teradata SQL-H
‣ Microsoft Polybase
‣ Hadapt

21. Data Movement to the Cloud
‣Use Windows Azure Blob Storage
• Already stored in 3 copies
• Hadoop can read from Azure blob storage
• Allows you to upload while using no Hadoop network or CPU resources
‣Compress files
• Hadoop can read Gzip
• Uses less network resources than uncompressed
• Costs less for direct storage costs
• Compress directories where source files are created as well.
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22. ‣ What is a Big Data approach to Analytics?
‣ Massive scale
‣ Data discovery & research
‣ Self-service
‣ Reporting & BI
‣ Why do we take this Big Data Analytics approach?
‣ TBs of change data in each subject area
‣ The data in the sources are variable and unstructured
‣ SSIS ETL alone couldn’t keep up or handle complexity
‣ SQL Server 2012 columnstore and tabular SSAS 2012 are key to using SQL
Server for Big Data
‣ With the configs mentioned previously, SQL Server works great
‣ Analytics on Big Data also requires Big Data Analytics tools
‣ Aster, Tableau, PowerPivot, SAS, Parallel Data Warehouse
Wrap-up