This document discusses big data and SQL Server. It covers what big data is, the Hadoop environment, big data analytics, and how SQL Server fits into the big data world. It describes using Sqoop to load data between Hadoop and SQL Server, and SQL Server features for big data analytics like columnstore and PolyBase. The document concludes that a big data analytics approach is needed for massive, variable data, and that SQL Server 2012 supports this with features like columnstore and tabular SSAS.

1. Big Data with SQL Server
Philly Code Camp
November 2012
Mark Kromer
BI & Big Data Technology Director
http://www.kromerbigdata.com
@kromerbigdata
@mssqldude

2. What we’ll (try) to cover today
‣ What is Big Data?
‣ The Big Data and Apache Hadoop environment
‣ Big Data Analytics
‣ SQL Server in the Big Data world
‣ How we utilize Big Data @ Razorfish
2

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. MapReduce Framework (Map)
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);
}
}

6. MapReduce Framework (Reduce & Job)
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;
}
}

7. Mark’s Big Data Myths
‣ 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

8. Razorfish & Big Data
‣ Web Analytics
‣ Big Data Analytics
‣ Digital Marketing – Ad Server Analytics
‣ Multiple TBs of online data per client per year
‣ Elastic Web-scale MapReduce & Hadoop
‣ Increase ROI of digital marketing campaigns

9. Big Data Analytics Web Platform

10. In-Database Analytics (Teradata Aster)
• Because of built-in analytics functions and big data performance, Aster becomes
the data scientist’s sandbox and BI’s big data analytics processor.
Prepackaged Analytics
Functions (including Attribution)

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

12. Sqoop
Data transfer to & from Hadoop & SQL Server
‣ 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.

13. SQL Server Big Data Environment
‣ SQL Server Database
‣ SQL Server 2008 R2 or 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 2008 R2 or 2012 Enterprise Edition
‣ 2008 R2 OLAP cubes partition-aligned with DW
‣ 2012 cubes in-memory tabular cubes
‣ All access through MSMDPUMP or SharePoint

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

15. Wrap-up
‣ What is a Big Data approach to Analytics?
‣ Massive scale
‣ Data discovery & research
‣ Self-service
‣ Reporting & BI
‣ Why did we take this Big Data Analytics approach?
‣ Each Web client produces an average of 6 TBs of ICA data in a year
‣ 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 were key to using SQL
Server for Big Data
‣ With the configs mentioned previously, SQL Server is working great
‣ Analytics on Big Data also requires Big Data Analytics tools
‣ Aster, Tableau, PowerPivot, SAS