Introduction to Big Data & Hadoop

www.edureka.co/big-data-and-hadoop
Introduction to big data and hadoop

At the end of this session , you will understand the:
→ Big Data Learning Paths
→ Big Data Introduction
→ Hadoop and Its Eco-System
→ Hadoop Architecture
→ Next Step on How to Setup Hadoop

• Java / Python / Ruby
• Hadoop Eco-system
• NoSQL DB
• Spark
• Linux Administration
• Cluster Management
• Cluster Performance
• Virtualization
• Statistics Skills
• Machine Learning
• Hadoop Essentials
• Expertise in R
Developer/Testing
Administration
Data Analyst
Big Data and Hadoop
MapReduce
Design Patterns
Apache
Spark & Scala
Apache Cassandra
Linux Administration Hadoop Administration
Data Science
Business Analytics
Using R
Advance Predictive
Modelling in R
Talend for Big Data
Data Visualization
Using Tableau

→ Lots of Data (Terabytes or Petabytes)
→ Big data is the term for a collection of data sets so
large and complex that it becomes difficult to process
using on-hand database management tools or
traditional data processing applications
→ The challenges include capture, curation, storage,
search, sharing, transfer, analysis, and visualization Big Data

→ Systems / Enterprises generate huge amount of data from Terabytes to Petabytes of information
Stock market generates about one terabyte of new trade data per day to
perform stock trading analytics to determine trends for optimal trades

→ By 2020, IDC (International Data Corporation) predicts the number will have reached 40,000 EB, or 40 Zettabytes (ZB)
→ The world’s information is doubling every two years. By 2020 the world will generate 50 times the amount of
information and 75 times the number of information containers

IBM’s Definition – Big Data Characteristics
http://www-01.ibm.com/software/data/bigdata/
VOLUME
Web
logs
Images
Videos
Audios
Sensor
Data
VARIET
Y
VELOCITY VERACITY
Min Max Mean SD
4.3 7.9 5.84 0.83
2.0 4.4 3.05 0.43
0.1 2.5 1.20 0.76

Hello There!!
My name is Annie.
I love quizzes and
puzzles and I am here to
make you guys think and
answer my questions.

Map the following to corresponding data type:
» XML files, e-mail body
» Audio, Video, Images, Archived documents
» Data from Enterprise systems (ERP, CRM etc.)

Ans. XML files, e-mail body → Semi-structured data
Audio, Video, Image, Files, Archived documents → Unstructured data
Data from Enterprise systems (ERP, CRM etc.) → Structured data

More on Big Data
• http://www.edureka.in/blog/the-hype-behind-big-data/
Why Hadoop?
• http://www.edureka.in/blog/why-hadoop/
Opportunities in Hadoop
• http://www.edureka.in/blog/jobs-in-hadoop/
Big Data
• http://en.wikipedia.org/wiki/Big_Data
IBM’s definition – Big Data Characteristics
• http://www-01.ibm.com/software/data/bigdata/

Slide 13Slide 13 www.edureka.co/big-data-and-hadoop
→ Web and e-tailing
» Recommendation Engines
» Ad Targeting
» Search Quality
» Abuse and Click Fraud Detection
→ Telecommunications
» Customer Churn Prevention
» Network Performance Optimization
» Calling Data Record (CDR) Analysis
» Analysing Network to Predict Failure
http://wiki.apache.org/hadoop/PoweredBy

→ Government
» Fraud Detection and Cyber Security
» Welfare Schemes
» Justice
→ Healthcare and Life Sciences
» Health Information Exchange
» Gene Sequencing
» Serialization
» Healthcare Service Quality Improvements
» Drug Safety

→ Banks and Financial services
» Modeling True Risk
» Threat Analysis
» Fraud Detection
» Trade Surveillance
» Credit Scoring and Analysis
→ Retail
» Point of Sales Transaction Analysis
» Customer Churn Analysis
» Sentiment Analysis

→ Insight into data can provide Business Advantage.
→ Some key early indicators can mean Fortunes to Business.
→ More Precise Analysis with more data.
*Sears was using traditional systems such as Oracle Exadata, Teradata and SAS etc., to store and process the customer activity and sales data.
Case Study: Sears Holding Corporation

Mostly Append
BI Reports + Interactive Apps
RDBMS (Aggregated Data)
ETL Compute Grid
Storage only Grid (Original Raw Data)
Collection
Inctrumentation
A meagre
10% of the
~2PB data is
available for
BI
Storage
2. Moving data to compute doesn’
t scale
90% of
the ~2PB
archived
Processing
3. Premature data
death
1. Can’t explore original
high fidelity raw data

Mostly Append
BI Reports + Interactive Apps
RDBMS (Aggregated Data)
Hadoop : Storage + Compute Grid
Collection
Instrumentation
Both
Storage
And
Processing
Entire ~2PB
Data is
available for
processing
No Data
Archiving
1. Data Exploration &
Advanced analytics
2. Scalable throughput for ETL &
aggregation
3. Keep data alive
forever
*Sears moved to a 300-Node Hadoop cluster to keep 100% of its data available for processing rather than a meagre 10% as
was the case with existing Non-Hadoop solutions.

Read 1 TB Data
4 I/O Channels
Each Channel – 100 MB/s
1 Machine
4 I/O Channels
10 Machine

4 I/O Channels
1 Machine
4 I/O Channels
10 Machine
43 Minutes
Read 1 TB Data

4 I/O Channels
1 Machine
4 I/O Channels
10 Machine
4.3 Minutes43 Minutes
Read 1 TB Data

→ Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters
of commodity computers using a simple programming model.
→ It is an Open-source Data Management with scale-out storage and distributed processing.

Hadoop is a framework that allows for the distributed
processing of:
» Small Data Sets
» Large Data Sets

Ans. Large Data Sets.
It is also capable of processing small data-sets. However, to
experience the true power of Hadoop, one needs to have
data in TB’s. Because this is where RDBMS takes hours and
fails whereas Hadoop does the same in couple of minutes.

Pig Latin
Data Analysis
Hive
DW System
Other YARN
Frameworks
(MPI, GRAPH)
HBaseMapReduce Framework
YARN
Cluster Resource Management
Apache Oozie
(Workflow)
HDFS
(Hadoop Distributed File System)
Hadoop 2.0
Sqoop
Unstructured or
Semi-structured Data Structured Data
Flume
Mahout
Machine Learning

DataNode
Node
Manager
DataNode DataNode DataNode
YARN
HDFS
Cluster
Resource
Manager
NameNode
Node
Manager
Node
Manager
Node
Manager

RAM: 16GB
Hard disk: 6 x 2TB
Processor: Xenon with 2 cores
Ethernet: 3 x 10 GB/s
OS: 64-bit CentOS
RAM: 16GB
Hard disk: 6 x 2TB
Processor: Xenon with 2 cores.
OS: 64-bit CentOS
RAM: 64 GB,
Hard disk: 1 TB
Processor: Xenon with 8 Cores
OS: 64-bit CentOS
Power: Redundant Power Supply
RAM: 32 GB,
Hard disk: 1 TB
OS: 64-bit CentOS
Active NameNodeSecondary NameNode
DataNode DataNode
RAM: 64 GB,
Hard disk: 1 TB
OS: 64-bit CentOS
StandBy NameNode

Master
NameNode
http://master:50070/
ResourceManager
http://master:8088
Slave01
DataNode
NodeManager
Slave02
DataNode
NodeManager
Slave03
DataNode
NodeManager
Slave04
DataNode
NodeManager
Slave05
DataNode
NodeManager

NodeManager
DataNode
NodeManager
HDFS YARN
NameNode
DataNode
NodeManager DataNode
ResourceManager
DataNode
NodeManager
DataNode
NodeManager
NodeManager
DataNode
NodeManager
DataNode
NodeManager
DataNode

Namenode NS
Storage
…
NamespaceBlockStorage
Namespace
NN-1 NN-k NN-n
Common Storage
BlockStorage
Pool 1 Pool k Pool n
Block Pools
… …
Hadoop 1.0 Hadoop 2.0
DatanodeDatanode
Datanode 1
…
Datanode m
…
Datanode 2
…
Block Management

How does HDFS Federation help HDFS Scale horizontally?
a. Reduces the load on any single NameNode by using the multiple,
independent NameNode to manage individual parts of the file system
namespace.
b. Provides cross-data centre (non-local) support for HDFS, allowing a
cluster administrator to split the Block Storage outside the local cluster.

Ans. Option (a)
In order to scale the name service horizontally, HDFS federation
uses multiple independent NameNode. The NameNode are
federated, that is, the NameNode are independent and don’t
require coordination with each other.

You have configured two name nodes to manage /marketing and
/finance respectively. What will happen if you try to put a file in
/accounting directory?

Ans. Put will fail. None of the namespace will manage the file and you
will get an IOException with a No such file or directory error.

Node Manager
Container
App
Master
Node Manager
Container
App
Master
HDFS YARN
Resource
Manager
All name space edits
logged to shared NFS
storage; single writer
(fencing)
Read edit logs and
applies to its own
namespace
Secondary
Name Node
DataNode
Standby
NameNode
Active
NameNode
DataNode Data Node
DataNodeDataNode
NameNode
High
Availability
Next Generation
MapReduce
*Not necessary to
configure
Secondary
NameNode
Client
Shared Edit Logs
HDFS HIGH AVAILABILITY
Node Manager
Container
App
Master
Node Manager
Container
App
Master

HDFS HA was developed to overcome the following disadvantage in Hadoop
1.0?
a. Single Point of Failure of Name-Node
b. Only one version can be run in classic Map-Reduce
c. Too much burden on Job Tracker

Ans. Single Point of Failure of NameNode

…

Facebook
→ We use Hadoop to store copies of internal log and dimension data sources and use
it as a source for reporting/analytics and machine learning.
→ Currently we have 2 major clusters:
» A 1100-machine cluster with 8800 cores and about 12 PB raw storage.
» A 300-machine cluster with 2400 cores and about 3 PB raw storage.
» Each (commodity) node has 8 cores and 12 TB of storage.
» We are heavy users of both streaming as well as the Java APIs. We have built
a higher level data warehousing framework using these features called Hive
(see the http://Hadoop.apache.org/hive/). We have also developed a FUSE
implementation over HDFS.

Hadoop can run in any of the following three modes:
Fully-Distributed Mode
Pseudo-Distributed Mode
→ No daemons, everything runs in a single JVM.
→ Suitable for running MapReduce programs during development.
→ Has no DFS.
→ Hadoop daemons run on the local machine.
→ Hadoop daemons run on a cluster of machines.
Standalone (or Local) Mode

→ Apache Hadoop and HDFS
→ http://www.edureka.in/blog/introduction-to-apache-hadoop-hdfs/
→ Apache Hadoop HDFS Architecture
→ http://www.edureka.in/blog/apache-hadoop-hdfs-architecture/

• Referring the documents present in the LMS under assignment solve the below problem.
How many such DataNodes you would need to read 100TB data in 5 minutes in your Hadoop Cluster?

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Introduction to Big Data & Hadoop

Introduction to Big Data & Hadoop

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Introduction to Big Data & Hadoop