Complete Course – Big Data Hadoop

What will you learn from this Big Data Hadoop – The Complete Course?

  • Complete understanding about the Big Data Hadoop
  • Become proficient while using Map Reduce, Impala, Apache Pig, ZooKeeper, HDFS, Hadoop, Hive, etc.
  • Get Big Data Hadoop Certifications
  • Attain real-world skills that are needed to get a respected job in a multinational IT company

Big Data Hadoop – The Complete Course Requirements

  • The students should have a device where they can access the internet.
  • There is no need for any pre-knowledge for signing up for this course.
  • No requirement for any type of software
  • Individuals should have a strong desire to learn Big Data Hadoop

Program Details

As per the survey of the Forbes, the Big Data & Hadoop become a $100 billion industry by the year 2022. Presently, it has been growing at a CAGR of 42% since 2015, which is truly incredible. According to a prediction made by the McKinsey, there will be a shortage of over 1.5 million data experts by 2018. The present salary of the Big Data Hoop Developers is around $135,000, which is a big amount. You can even check that on Indeed.

We have created “Big Data Hadoop – The Complete Course” to help the students who have a strong desire to learn. It will cover all the important topics that range from the basic level to the advanced techniques required for getting Big Data Hadoop Certification. It is also a must-have course for the Big Data experts who want to improve their knowledge. This course will cover Apache Hive, Impala, Scoop, ZooKeeper, Yarn, Hadoop, HDFS, PIG, Map Reduce, and much more.

So, what are waiting for? Come on, guys! Sign up for this course now and have a secure career.

Who is the target audience?

  • Starters who are looking to learn about the Big Data Hadoop
  • Professionals who want to get Big Data Hadoop Certification

Starting Course

1
Big Data Introduction
2
Big Data Concept Part 1
3
Big Data Concept Part 2
4
Big Data Benefits Part 1
5
Big Data Benefits Part 2
6
Data Storage & Analysis
7
Querying data
8
Grid computing
9
Big Data
10
Important Note for Exercises
11
Query a public dataset
12
Creating a dataset
13
Querying a table
14
Big table instance
15
Pub-Sub
16
Hadoop Introduction
17
Hadoop Features
18
HDFS Architecture Part 1
19
HDFS Architecture Part 2
20
HDFS Architecture Part 3
21
HDFS Components
22
HDFS Client
23
HDFS Components Part 2
24
HDFS Components Part 3
25
HDFS Components Part 4
26
HDFS Client creating new file
27
Rack Description
28
HDFS Write Operation
29
Selection of Data Nodes & Node Distance
30
Serialization
31
HDFS Blocks
32
HDFS Caching & Failover
33
HDFS Federation
34
HDFS High Availability
35
Hadoop Archive files
36
Hadoop Releases
37
Hadoop 2.0 features
38
Cluster size specifications
39
Master Node scenario
40
Network Topology
41
Cluster Setup & Installation
42
Configuration Management
43
HDFS Data Integrity
44
Cycle of Big Data Management
45
Cycle of Big Data Management 2
46
Big data in the Cloud
47
Hadoop
48
Creating Cluster
49
HUE HDFS File Browser
50
HDFS File Browser 2
51
Cloud SQL Instance
52
Data Store Query
53
Google Storage
54
Map Reduce Introduction
55
Map Reduce Part 1
56
Map Reduce Part 2
57
Map Reduce Phases
58
Map Reduce Part 3
59
Job Tracker Part 1
60
Job Tracker Part 2
61
Job Tracker Part 3
62
Anatomy of Map Reduce Program
63
Map Reduce Data Types
64
Map Reduce Data Types 2
65
Resource Manager Failure
66
Map Reduce
67
Submit Job
68
Submit Job 2
69
HUE Job Designer
70
HUE METASTORE MANAGER
71
YARN
72
YARN Processing
73
Yarn
74
HIVE
75
HIVE Basics
76
HIVE Basics Part 2
77
HIVE Architecture Part 1
78
HIVE
79
HIVE QUERY
80
Apache PIG Introduction
81
PIG modes
82
Comparison of PIG, HIVE , Map Reduce
83
PIG
84
Data ingestion
85
Query editors
86
Components of Impala Server
87
Impala Statestore
88
Impala catalog service
89
Job Designer
90
Impala
91
HUE IMPALA Query
92
Sqoop
93
Sqoop Import Export
94
Zoo Keeper
95
Zoo keeper services part 1
96
Zoo keeper services part 2
97
Zoo keeper services part 3
98
Zoo Keeper
99
OOZIE
100
Installation of Apache Hadoop 2.7.3 on Ubuntu
Faq Content 1
Faq Content 2

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Enrolled: 53 students
Duration: 0 weeks
Lectures: 100
Video: 120 hours
Level: Advanced

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Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed