Big Data Hadoop Training Online Live Video Tutorials
The course is structured for beginners and mid-level IT engineers. Subscription provides a year long access to live video tutorials,…
The curriculum has been developed by professional trainers to guide you through this tutorial. Big Data Hadoop Training Online course starts with the basics of Big Data and Hadoop Distributed File System (HDFS) to continue on to the architecture of Hadoop and MapReduce framework. The tutorials teach you how to work with pig and oozie in Hadoop. Understanding HIVE concepts and measures to solve big data problems.
Trainer : Prasanna
25+ Hours of HQ Videos
Life Time Access to Big data hadoop Discussion Forums
Email & Forum Based Technical Support
Access to Big Data Hadoop Scripts, PDF’s, & Assignments
Pre-Requisites: Focused specifically for beginner to mid-level IT Engineers, QA professionals and Developers.
+ Knowledge on core java concepts would be really helpful in understanding the Hadoop framework
+ Scripting knowledge on perl , unix shell script would be helpful.
Pricing : – To Join the Live Training or Recorded Videos check the options given below.
-Payment link for Big Data Hadoop LIVE+Video Tutorials : $500
1 year of Video access plus Access to Live training.
-Payment link for Big Data Hadoop Video Tutorials : $450
If you are interested in the Video only option, then you are welcome to join anytime. You will get access to Big Data Hadoop Testing videos that are of high quality.
1 year of Recorded Video access.
Chapter 1 : Understanding Big Data and Hadoop
- 1.1 Understand Big Data
- 1.2 Limitations of the existing solutions for Big Data problem
- 1.3 How Hadoop solves the Big Data problem
- 1.4 The common Hadoop ecosystem components
- 1.5 Hadoop Architecture
- 1.6 HDFS
- 1.7 Anatomy of File Write and Read
- 1.8 Rack Awareness
Chapter 2 : Hadoop Architecture and HDFS
- 2.1 Hadoop Cluster Architecture
- 2.2 Important Configuration files in a Hadoop Cluster
- 2.3 Data Loading Techniques
Chapter 3 : Hadoop MapReduce Framework – I
- 3.1 Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS
Chapter 4 : Hadoop MapReduce Framework – II
- 4.1 Input Splits in MapReduce
- 4.2 Combiner & Partitioner and Demos on MapReduce using different data sets
Chapter 5 :Advanced MapReduce
- 5.1 Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format
- 5.2 How to deal with complex MapReduce programs.
Chapter 6 :Pig
- 6.1 Introduction to Pig
- 6.2 Types of use case we can use Pig
- 6.3 Tight coupling between Pig and MapReduce
- 6.4 Pig Latin scripting
Chapter 7 : Hive
- 7.1 Introduction to Hive concepts
- 7.2 Querying Data in Hive and Hive UDF
Chapter 8 : Oozie and Hadoop Project
- 8.1 Understand working of multiple Hadoop ecosystem components together in a Hadoop implementation to solve Big Data problems
- 8.2 We will discuss multiple data sets and specifications of the project.
- 8.3 Sqoop demo and Apache Oozie Workflow Scheduler for Hadoop Jobs.
Chapter 9 : Realtime Usescases
- 9.1 Realtime usecases along with sample datasets and running big data jobs