Introduction to Big Data and Hadoop
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Very knowledgeable and informative training. Basics are cleared.
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Training is good and professional as the name says its all "Simplilearn".
← | →
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Trainer was very interactive and energetic; very well explained the topic with scenarios provided with in depth knowledge.
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Course
Online

Description
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Type
Course
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Methodology
Online
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Reviews
-
Very knowledgeable and informative training. Basics are cleared.
← | →
-
Training is good and professional as the name says its all "Simplilearn".
← | →
-
Trainer was very interactive and energetic; very well explained the topic with scenarios provided with in depth knowledge.
← | →
Course rating
Recommended
Centre rating
Ankeeta Katkade
Ramkumar
Praveen Goud
Samvith Bhat
This centre's achievements
All courses are up to date
The average rating is higher than 3.7
More than 50 reviews in the last 12 months
This centre has featured on Emagister for 7 years
Course programme
- Introduction to Big Data and Hadoop
- Lesson 1.0 - Introduction to Big Data and Hadoop
- 1 Introduction to Big Data and Hadoop
- 2 Objectives
- 3 Need for Big Data
- 4 Three Characteristics of Big Data
- 5 Characteristics of Big Data Technology
- 6 Appeal of Big Data Technology
- 7 Handling Limitations of Big Data
- 8 Introduction to Hadoop
- 9 Hadoop Configuration
- 10 Apache Hadoop Core Components
- 11 Hadoop Core Components—HDFS
- 12 Hadoop Core Components—MapReduce
- 13 HDFS Architecture
- 14 Ubuntu Server—Introduction
- 15 Hadoop Installation—Prerequisites
- 16 Hadoop Multi-Node Installation—Prerequisites
- 17 Single-Node Cluster vs. Multi-Node Cluster
- 18 MapReduce
- 19 Characteristics of MapReduce
- 20 Real-Time Uses of MapReduce
- 21 Prerequisites for Hadoop Installation in Ubuntu Desktop 12.04
- 22 Hadoop MapReduce—Features
- 23 Hadoop MapReduce—Processes
- 24 Advanced HDFS–Introduction
- 25 Advanced MapReduce
- 26 Data Types in Hadoop
- 27 Distributed Cache
- 28 Distributed Cache (contd.)
- 29 Joins in MapReduce
- 30 Introduction to Pig
- 31 Components of Pig
- 32 Data Model
- 33 Pig vs. SQL
- 34 Prerequisites to Set the Environment for Pig Latin
- 35 Summary
- Lesson 1.1 - Hive HBase and Hadoop Ecosystem Components
- 1 Hive, HBase and Hadoop Ecosystem Components
- 2 Objectives
- 3 Hive—Introduction
- 4 Hive—Characteristics
- 5 System Architecture and Components of Hive
- 6 Basics of Hive Query Language
- 7 Data Model—Tables
- 8 Data Types in Hive
- 9 Serialization and De serialization
- 10 UDF/UDAF vs. MapReduce Scripts
- 11 HBase—Introduction
- 12 Characteristics of HBase
- 13 HBase Architecture
- 14 HBase vs. RDBMS
- 15 Cloudera—Introduction
- 16 Cloudera Distribution
- 17 Cloudera Manager
- 18 Hortonworks Data Platform
- 19 MapR Data Platform
- 20 Pivotal HD
- 21 Introduction to ZooKeeper
- 22 Features of ZooKeeper
- 23 Goals of ZooKeeper
- 24 Uses of ZooKeeper
- 25 Sqoop—Reasons to Use It
- 26 Sqoop—Reasons to Use It (contd.)
- 27 Benefits of Sqoop
- 28 Apache Hadoop Ecosystem
- 29 Apache Oozie
- 30 Introduction to Mahout
- 31 Usage of Mahout
- 32 Apache Cassandra
- 33 Apache Spark
- 34 Apache Ambari
- 35 Key Features of Apache Ambari
- 36 Hadoop Security—Kerberos
- 37 Summary
- Lesson 1.2 - Quiz
- Quiz
- Lesson 1.3 - Thank You
- Thank You
- Lesson 1.0 - Introduction to Big Data and Hadoop
Additional information
What is this course about?
The ‘Introduction to Big Data and Hadoop’ is an ideal course package for individuals who want to understand the basic concepts of Big Data and Hadoop. On completing this course, learners will be able to interpret what goes behind the processing of huge volumes of data as the industry switches over from excel-based analytics to real-time analytics.
The course focuses on the basics of Big Data and Hadoop. It further provides an overview of the commercial distributions of Hadoop as well as the components of the Hadoop ecosystem.
Introduction to Big Data and Hadoop
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