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Hands-on HADOOP Masterclass – Tame the Big Data!

Big Data, Hadoop, MapReduce, HDFS, HIVE, PIG, Mahout, NoSQL, Oozie, Flume, Storm, Avro, Spark, Sqoop, Cloudera and more
Instructor:
EDU CBA
17,607 students enrolled
English [Auto-generated]
Learn the concepts of Hadoop and Big Data
Learn in details the concepts of MapReduce, HDFS, HIVE, PIG
Learn Mahout, NoSQL, Oozie, Flume, Storm, Avro, Spark, Sqoop, Cloudera and more
Perform Data Analytics using Hadoop
Master the concepts of Hadoop framework
Get experience on different configurations of Hadoop cluster
Work with real-time projects using Hadoop

Learn from well crafted study materials on Big Data, Hadoop, MapReduce, HDFS, HIVE, PIG, Mahout, NoSQL, Oozie, Flume, Storm, Avro, Spark, Sqoop, Cloudera, Data Analysis, Survey Analysis, Data Management, Sales Analysis, salary Analysis, Traffic Analysis, Loan Analysis, Log Data Analysis, Youtube Data Analysis, Sensor Data Analysis. Learn by doing. Learn from hands-on examples of analyzing big data. Turn your Crafting ability which can be a mixed bag ranging from developers to data scientists using procedural languages in the Hadoop space. Discover and learn the fundamentals of Hadoop. Be a person comfortable in managing the development and deployment of Hadoop applications.

What is Big Data

Big data is a collection of large datasets which cannot be processed using the traditional techniques. Big data uses various tools and techniques to collect and process the data. Big data deals with all types of data including structured, semi-structured and unstructured data. Big data is used in various fields data like

  • Black box data

  • Social media data

  • Stock exchange data

  • Power Grid Data

  • Transport Data

  • Search Engine Data

Benefits of Big Data

Big data has become very important and it is emerging as one of the crucial technologies in today’s world. The benefits of big data are listed below

Big data can be used by the companies to know the effectiveness of their marketing campaigns, promotions and other advertising media

Big data helps the companies to plan their production

Using the information provided through Big data companies can deliver better and quick service to their customers

Big data helps in better decision making in the companies which will increase the operational efficiencies and reduces the risk of the business

Big data handles huge volume of data in real time and thus enables data privacy and security to a great extent

Challenges faced by Big Data

The major challenges of big data are as follows

  • Curation

  • Storage

  • Searching

  • Transfer

  • Analysis

  • Presentation

What is Hadoop

Hadoop is an open source software framework which is used for storing data of any type. It also helps in running applications on group of hardware. Hadoop has huge processing power and it can handle more number of tasks. Open source software here means it is free to download and use. But there are also commercial versions of Hadoop which is becoming available in the market. There are four basic components of Hadoop – Hadoop Common, Hadoop Distributed File System (HDFS), MapReduce and Yet Another Resource Negotiator (YARN).

Benefits of Hadoop Course

Hadoop is used by most of the organizations because of its ability to store and process huge amount of any type of data. The other benefits of Hadoop includes

  • Computing Power

  • Flexibility

  • Fault Tolerance

  • Low Cost

  • Scalability

Uses of Hadoop

Hadoop is used by many of the organization’s today because of its following uses

Low cost storage and active data archive

Staging area for a data warehouse and analytics store

Data lake

Sandbox for discovery and analysis

Recommendation Systems

Big Data and Hadoop Training Introduction

1
Introduction to Big Data Hadoop
2
Scenario of Big Data Hadoop
3
Write Anatomy
4
Continuation os Write Anatomy
5
Read Anatomy
6
Continuation os Read Anatomy
7
Word Count in Hadoop
8
Running Hadoop Application
9
Continuation Hadoop Application
10
Working on Sample Program
11
Creating Method Map
12
Iterable Values
13
Output Path
14
Scary Catch Box

Hadoop Architecture and HDFS

1
Introduction to Hadoop Admin
2
Limitations of Existing System
3
Hadoop Key Characteristics
4
Hadoop Distributed File System
5
Storage Layer of Hadoop
6
Hadoop 1.0 Core Components
7
FS Images
8
Secondary Name Node
9
HDFC Architecture
10
Block Placement Policy
11
Assignments
12
Hadoop Architecture Cluster Setup
13
Installation of Hadoop in Vmware Workstation
14
Hadoop Package Installation
15
Configuration of Host Name and Gateway
16
Copying of ISO File to Centos
17
Installation of SSH File Using Yum
18
Copy the Public Key to Authorized Key in SSH
19
Setup for Block Size and Mapped
20
Create SSH -keygen for HD User
21
Start the Map Reduce in Hadoop
22
Creating a Clone for Hadoop
23
Changing the Hostname
24
Configuring Hadoop Site
25
Slave File Configuration
26
Creating Name node and Data Node In Hadoop
27
Understanding HDFS
28
Hadoop Core Config Files
29
Hadoop Cluster and Password less SSH
30
Configuring Rack Awareness
31
Configuring Rack Awareness Continues
32
Running DFS Admin Report
33
Hadoop Map Reduce
34
Running Hadoop NameNode
35
Executing Hadoop Command
36
Writing File in Hadoop Cluster
37
Understanding FS Command
38
Directories of Data
39
Fie System Check
40
Writing Data in HDFS
41
Checkpointing Node
42
Merging the Metadata
43
Cluster in Safe Mode
44
Cluster in Maintainance Mode
45
Commissioning of Data Nodes
46
Name Node
47
Validating the Data Node
48
Storage Considerations

MapReduce Fundamentals

1
Secondary Sort Hadoop
2
Creating Composite Key
3
Continue on Composite Key
4
Word Count Group
5
Importance of Partition
6
Hadoop FS - LS
7
Joins in Hadoop
8
Creating Configuration Object
9
Setup Method
10
Map Side Join Mapper
11
Hadoop Commands
12
Combiner in Hadoop
13
Continue on Combiner in Hadoop
14
Uploading Combiner Jar
15
Introduction to Real World
16
Ratings Mapper
17
Movie and Ratings Runner
18
Movie and Rating Calc Jar
19
Total Ratings By A User
20
User Rating Reducer
21
User Rating Class
22
Yarn Basic Tutorial
23
Node Manager

MapReduce Advanced

1
Running a MapReduce Program
2
Running a MapReduce Program Continues
3
HDFS File System
4
Combination of Word Count Functionality
5
Word Count With Tools
6
Log Processor
7
Advanced MapReduce and PIG
8
More on Advanced MapReduce
9
Executing Similar Program
10
HDI Data and Export Data
11
Creating New Java Class
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