The book 'HDInsight Essentials' by Rajesh Nadipalli provides a comprehensive overview of Microsoft's HDInsight and its integration with Hadoop, focusing on both cloud and on-premise setups. It serves as a quick reference guide covering various chapters on deployment, administration, data ingestion, transformation, and analysis with practical examples. Key differentiators for HDInsight include its enterprise readiness, compatibility with Microsoft tools, and streaming capabilities.

1. HDInsight
Essentials
ISBN
:
1849695369
/
ISBN
13
:
9781849695367
Rajesh
Nadipalli
05/01/2014

2. Goals
of
this
Book
• Focus
on
Microso'’s
new
Hadoop
distribu=on
• Serve
as
Quick
Reference
• Provide
an
Overview
of
Hadoop
• Address
both
cloud
and
on-­‐premise
setup
for
HDInsight
• Highlight
HDInsight
differen:ator
• Provide
Prac=cal
&
Real
world
examples

3. Book
Table
of
Contents
• Chapter
1:
HDInsight
in
a
Heartbeat
• Chapter
2:
Deployment
HDInsight
on
premise
• Chapter
3:
HDInsight
Azure
cloud
service
• Chapter
4:
Administer
your
cluster
• Chapter
5:
Ingest
data
to
your
cluster
• Chapter
6:
Transform
data
in
your
cluster
• Chapter
7:
Analyze
&
Report
data
from
cluster
• Chapter
8:
Project
Planning
&
Architectural
Considera=ons

4. CHAPTER
1
HIGHLIGHTS:
HDINSIGHT
IN
A
HEARTBEAT

5. Big
Data
Problem
Characteristics

6. Hadoop
Overview
Self Healing
Distributed Storage
Fault Tolerant
Distributed
Computing
+
Abstraction for
Parallel Processing
CORE HADOOP COMPONENTS • HDFS:
Distributed
Storage
–
replicated,
self-­‐healing
and
scalable
• MapReduce:
Parallel
Processing,
process
local
data
for
efficiency

7. NameNode
JobTracker
TaskTracker
TaskTracker
TaskTracker
MapReduce
Layer
Distributed
File
System
Layer
Secondary
NameNode
Master
Node
Slaves
Nodes
DataNode
DataNode
DataNode
Hadoop
Nodes
Layout

8. Data
Sources
RDBMS
Databases
Audio,
Images
Log
Files
Sensors,
RFID
Social
Media,
Feeds
Hadoop
Data
Store
HDFS
Hbase
(NOSQL
DB)
Data
Processing
Mapreduce
Data
Access
Hive
Pig
Mahout
Machine
Learning
Flume,
Sqoop
Excel
Business
Data
Feeds
Zookeeper
(Distributed
Process
Management)
Hcatalog
(Metadata
on
Pig,
Hive,
MapReduce
)
Oozie
Workflow,
Scheduler
Infrastructure
,
Opera:ons
(Monitoring,
Configura<on)
Hadoop
Eco
System

9. Collect & Import
to HDFS
Process
(MapReduce)
Analyze
(BI Tools)
Report & Publish
End
to
End
Solution
on
Hadoop

10. Popular
Hadoop
Distributions
• Amazon
Elas=c
MapReduce
(cloud,
hbp://aws.amazon.com/
elas=cmapreduce/)
• Cloudera
(
hbp://www.cloudera.com/content/cloudera/en/home.html)
• EMC
PivitolHD
(hbp://gopivotal.com/)
• Hortonworks
HDP
(hbp://hortonworks.com/)
• MapR
(hbp://mapr.com/)
• Microsod
HDInsight
(cloud,
hbp://www.windowsazure.com/)

11. HDInsight
Differenciator
• Enterprise-­‐ready
Hadoop
backed
by
Microsod
• Analy:cs
using
Excel
• Integra=on
with
Ac=ve
Directory.
• Integra=on
with
.NET
and
Javascript
• Connectors
to
RDBMS
• Scale
using
cloud
offering:
Azure
HDInsight
service
enables
customers
to
scale
quickly
and
has
seamless
interface
between
HDFS
and
Azure
Storage
Vault
• JavaScript
Console

12. WordCount
in
HDInsight

13. CHAPTER
2
HIGHLIGHTS:
HDINSIGHT
INSTALL
ON
PREMISE

14. Apache
Hadoop
• Open
Source
Sodware
• Community
Development
Hortonworks
Data
PlaSorm
• Enterprise
Hadoop
Plagorm
(HDP)
• Leaders
in
Hadoop
• Code
commibers
to
Hadoop
Microso'
HDInsight
• Built
on
top
of
HDP
• Integra=on
with
ASV,
Excel,
Powerview,
SQLServer,
Ac=ve
Directory
HDInsight
Distribution

15. Physical
Install
Options
NN
SNN
JT
DN
/
TT
Single
node
for
development/test
Mul=
node
for
produc=on

16. Multi
Node
Install
Steps
• Pre-­‐requisites
• Networking
Setup
• Remote
Scrip=ng
• Firewall
Setup
• Sodware
Install
(each
node)
• Hadoop
Configura=on
• Verifica=on

17. CHAPTER
3
HIGHLIGHTS:
HDINSIGHT
AZURE
SERVICE

18. Azure
Cloud
Service
Create
Storage
Create
HDInsight
cluster

19. CHAPTER
4
HIGHLIGHTS:
ADMINISTER
YOUR
CLUSTER

20. HDInsight
Cluster
Management

21. HDInsight
Dashboard

22. HDInsight
Dashboard

23. NameNode
Status

24. Jobtracker
Status

25. CHAPTER
5
HIGHLIGHTS:
INGEST
DATA
INTO
YOUR
CLUSTER

26. Loading
Data
into
your
Cluster
You
have
following
op=ons…
• Loading
data
using
Hadoop
commands
• Loading
data
using
Azure
Storage
Vault
• Loading
data
using
Interac:ve
JavaScript
• Shipping
data
to
your
Cluster
• Loading
data
from
RDBMS
via
Sqoop

27. Loading
via
Azure
Storage
Explorer

28. CHAPTER
6
HIGHLIGHTS:
TRANSFORM
YOUR
DATA

29. Transforming
Data
You
have
following
op=ons…
• MapReduce
• Hive
• Pig
• Others

30. Processing
Data
in
Cluster
Map for
Jan2012
Map for
Feb2012
Map for
Apr2013
…
One Reducer

31. HDFS
Hive
JDBC/OBDC
Metastore
Thrift Server
Command LineWeb GUI
Driver
(Parser, Planner, Executor)
MapReduce
Hive

32. Raw
Data
in
HDFS
• Distributed
Storage
• Reliable
Data
Processing
via
Pig
• Pipelines
• Itera=ve
Processing
• Research
Data
Warehouse
HDFS
Data
Warehouse
via
Hive
• BI
Tools
• Analysis
Hive
or
Pig?

33. CHAPTER
7
HIGHLIGHTS:
ANALYZE
&
REPORT

34. Analyze
using
Excel

35. Analyze
using
Excel

36. CHAPTER
8:
PROJECT
PLANNING
&
ARCHITECTURAL
CONSIDERATIONS

37. Execu:ve
&
Stakeholder
Buy-­‐in
Discovery
&
Analysis
Design
Implementa:on
User
Acceptance
Produc:on
Opera:ons
Feedback,
New
Requirements