1. Big Data Analytics Projects
in the Real World
Mark Kromer
Pentaho Big Data Analytics Product Manager
@mssqldude
@kromerbigdata
http://www.kromerbigdata.com

2. 1. The Big Data Technology Landscape
2. Big Data Analytics
3. Big Data Analytics Scenarios:
❯ Digital Marketing Analytics
• Hadoop, Aster Data, SQL Server
❯ Sentiment Analysis
• MongoDB, SQL Server
❯ Data Refinery
• Hadoop, MPP, SQL Server, Pentaho
4. SQL Server in the Big Data world (Quasi-Real World)
What we’ll (try) to cover today

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. 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
❯ NoSQL does not have to be Big Data
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

5. • 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
Hadoop 1.x

6. © Hortonworks Inc. 2014
Hadoop 1 vs Hadoop 2
HADOOP 1.0
HDFS
(redundant, reliable storage)
MapReduce
(cluster resource management
& data processing)
HDFS2
(redundant, highly-available & reliable storage)
YARN
(cluster resource management)
MapReduce
(data processing)
Others
HADOOP 2.0
Single Use System
Batch Apps
Multi Purpose Platform
Batch, Interactive, Online, Streaming, …

7. © Hortonworks Inc. 2014
YARN: Taking Hadoop Beyond Batch
Applications Run Natively in Hadoop
HDFS2 (Redundant, Reliable Storage)
YARN (Cluster Resource Management)
BATCH
(MapReduce)
INTERACTIVE
(Tez)
STREAMING
(Storm, S4,…)
GRAPH
(Giraph)
IN-MEMORY
(Spark)
HPC MPI
(OpenMPI)
ONLINE
(HBase)
OTHER
(Search)
(Weave…)
Store ALL DATA in one place…
Interact with that data in MULTIPLE WAYS
with Predictable Performance and Quality of Service

8. © Hortonworks Inc. 2014
Tez – Introduction
1. Distributed execution framework targeted towards
data-processing applications.
2. Based on expressing a computation as a dataflow
graph.
3. Highly customizable to meet a broad spectrum of
use cases.
4. Built on top of YARN – the resource management
framework for Hadoop.
5. Open source Apache incubator project and Apache
licensed.

9. © Hortonworks Inc. 2014
Tez – Deep Dive – DAG API
DAG dag = new DAG();
Vertex map1 = new Vertex(MapProcessor.class);
Vertex map2 = new Vertex(MapProcessor.class);
Vertex reduce1 = new Vertex(ReduceProcessor.class);
Vertex reduce2 = new Vertex(ReduceProcessor.class);
Vertex join1 = new Vertex(JoinProcessor.class);
…….
Edge edge1 = Edge(map1, reduce1, SCATTER_GATHER, PERSISTED, SEQUENTIAL,
MOutput.class, RInput.class);
Edge edge2 = Edge(map2, reduce2, SCATTER_GATHER, PERSISTED, SEQUENTIAL, MOutput.class, RInput.class);
Edge edge3 = Edge(reduce1, join1, SCATTER_GATHER, PERSISTED, SEQUENTIAL, MOutput.class, RInput.class);
Edge edge4 = Edge(reduce2, join1, SCATTER_GATHER, PERSISTED, SEQUENTIAL, MOutput.class, RInput.class);
…….
dag.addVertex(map1).addVertex(map2)
.addVertex(reduce1).addVertex(reduce2)
.addVertex(join1)
.addEdge(edge1).addEdge(edge2)
.addEdge(edge3).addEdge(edge4);
reduce
1
map2
reduce
2
join1
map1
Scatter_Gather
Bipartite
Sequential
Scatter_Gather
Bipartite
Sequential
Simple DAG definition API

10. © Hortonworks Inc. 2014
YARN Eco-system
Page 10
Applications Powered by YARN
Apache Giraph – Graph Processing
Apache Hama - BSP
Apache Hadoop MapReduce – Batch
Apache Tez – Batch/Interactive
Apache S4 – Stream Processing
Apache Samza – Stream Processing
Apache Storm – Stream Processing
Apache Spark – Iterative applications
Elastic Search – Scalable Search
Cloudera Llama – Impala on YARN
DataTorrent – Data Analysis
HOYA – HBase on YARN
Frameworks Powered By YARN
Apache Twill
REEF by Microsoft
Spring support for Hadoop 2

11. Apache Spark
High-Speed In-Memory Analytics over Hadoop
● Open Source
● Alternative to Map Reduce for certain applications
● A low latency cluster computing system
● For very large data sets
● May be 100 times faster than Map Reduce for
– Iterative algorithms
– Interactive data mining
● Used with Hadoop / HDFS
● Released under BSD License

12. Popular Hadoop Distributions

13. Popular NoSQL Distributions
Transactional-based, not analytical schemas

14. Popular MPP Distributions
Big Data as distributed, scale-out, sharded data stores

15. Big Data Analytics Web Platform – RA 1

16. Sentiment Analysis
Reference Architecture 2
Big Data
Platforms
Hadoop
PDW
MongoDB
Social Media
Sources
Data
Orchestration
Data Models
Analytical
Models
OLAP Cubes
Data Mining
OLAP
Analytics
Tools,
Reporting
Tools,
Dashboards

17. Big Data Analytics

18. • Distributed Data (Data Locality)
❯ HDFS / MapReduce
❯ YARN / TEZ
❯ Replicated / Sharded Data
• MPP Databases
❯ Vertica, Aster, PDW, Greenplum … In-database analytics that can scale-out with
distributed processing across nodes
• Distributed Analytics
❯ SAS: Quickly solve complex problems using big data and sophisticated analytics in a
distributed, in-memory and parallel environment.”
http://www.sas.com/resources/whitepaper/wp_46345.pdf
• In-memory Analytics
❯ Microsoft PowerPivot (Tabular models)
❯ SAP HANA
❯ Tableau
Big Data Analytics
Core Tenets

19. 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)

20. 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)

21. 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

22. 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

23. 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

24. 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)

25. MongoDB and Enterprise IT Stack
EDWHadoop
Management&Monitoring
Security&Auditing
RDBMS
CRM, ERP, Collaboration, Mobile, BI
OS & Virtualization, Compute, Storage, Network
RDBMS
Applications
Infrastructure
Data Management
Online Data Offline Data

26. {
_id : ObjectId("4e2e3f92268cdda473b628f6"),
sourceIDs: {
ABCSystemIDPart1: 8397897,
ABCSystemIDPart2: 2937430,
ABCSystemIDPart3: 932018 }
accountType: “Checking”,
accountOwners: [
{ firstName : ”John",
lastName: “Smith”,
contactMethods: [
{ type: “phone”, subtype: “mobile”, number: 8743927394},
{ type: “mail”, address: “58 3rd St.”, city: …} ]
possibleMatchCriteria: {
govtID: 2938932432, fullName: “johnsmith”, dob: … } },
{ firstName : ”Anne",
maidenName: “Collins”,
lastName: “Smith”, …} ],
openDate: ISODate("2013-02-15 10:00”),
accountFeatures { Overdraft: true, APR: 20, … }
}
General document per customer per account
OR creditCardNumber: 8392384938391293
OR mortgageID: 2374389
OR policyID: 18374923

27. Text Search Example
(e.g. address typo so do fuzzy match)
// Text search for address filtered by first name and NY
> db.ticks.runCommand(
“text”,
{ search: “vanderbilt ave. vander bilt”,
filter: {name: “Smith”,
city: “New York”} })

28. //Find total value of each customer’s accounts for a given RM (or Agent) sorted by value
db.accts.aggregate(
{ $match: {relationshipManager: “Smith”}},
{ $group :
{ _id : “$ssn”,
totalValue: {$sum: ”$value”} }},
{ $sort: { totalValue: -1}} )
Aggregate: Total Value of Accounts

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

30. 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

31. SQL Server Big Data Analytics Features

32. DBA ETL/BI Developer Business Users & Executives Analysts & Data Scientists
OPERATIONAL DATA BIG DATA DATA STREAMPUBLIC/PRIVATE CLOUDS
Enterprise &
Interactive
Reporting
Interactive
Analysis
Dashboards Predictive
Analytics
Pentaho Business Analytics
Data Integration
Instaview | Visual Map Reduce
DIRECT ACCESS
Pentaho Big Data Analytics

33. Pentaho Big Data Analytics
Accelerate the time to big data value
• Full continuity from data
access to decisions –
complete data integration &
analytics for any big data
store
• Faster development,
faster runtime – visual
development, distributed
execution
• Instant and interactive
analysis – no coding and
no ETL required

34. Product Components
Pentaho Data Integration
• Visual development for big data
• Broad connectivity
• Data quality & enrichment
• Integrated scheduling
• Security integration
• Visual data exploration
• Ad hoc analysis
• Interactive charts & visualizations
Pentaho Dashboards
• Self-service dashboard builder
• Content linking & drill through
• Highly customized mash-ups
Pentaho Data Mining &
Predictive Analytics
• Model construction & evaluation
• Learning schemes
• Integration with 3rd part models
using PMML
Pentaho Enterprise &
Interactive Reports
• Both ad hoc & distributed reporting
• Drag & drop interactive reporting
• Pixel-perfect enterprise reports
Pentaho for Big Data
MapReduce & Instaview
• Visual Interface for Developing
MR
• Self-service big data discovery
• Big data access to Data Analysts
Pentaho Analyzer

35. ❯ Simple, easy-to-use visual data exploration
❯ Web-based thin client; in-memory caching
❯ Rich library of interactive visualizations
• Geo-mapping, heat grids, scatter plots, bubble
charts, line over bar and more
• Pluggable visualizations
❯ Java ROLAP engine to analyze structured and
unstructured data, with SQL dialects for querying
data from RDBMs
❯ Pluggable cache integrating with leading caching
architectures: Infinispan (JBoss Data Grid) &
Memcached
Pentaho Interactive Analysis & Data Discovery
Highly Flexible Advanced Visualizations