This course will begin with the big data motivation and explaining all the components in Hadoop including Hadoop cluster and distributed file systems. How to use HDFS distributed storage. What is map reduce and its effect in distributed processing. Write a count program in java and explore map reduce further in python.
Duration:
5 Days
Audience:
Hadoop Developer
Prerequisites:
basic computer skills, basic knowledge in programming
Course outline:
<p>Module 01 - Big Data. Motivation, Hadoop components</p>
<p>Module 02 - Using the Hadoop HDFS Distributed Storage</p>
<p>Module 03 - Distributed processing Map Reduce</p>
<p>Module 04 - Word count java program in Map Reduce</p>
<p>Module 05 - A Better Word count program</p>
<p>Module 06 - Map Reduce and other languages (a simple example in python</p>
<p>Hive -</p>
<p>Module 01. Hive – Basic Concepts</p>
<p><span style="font-size: 11px;">Module 02. Hive - Joins</span></p>
<p><span style="font-size: 11px;">Module 03 Hive - Partitions</span></p>
<p><span style="font-size: 11px;">Module 04 Hive - Bucketing and external tables</span></p>
<p><span style="font-size: 11px;">Module 05 Hive - Data pipeline version 1 (This is basic data warehouse)</span></p>
<p><span style="font-size: 11px;">Module 06 Hive - Data pipeline upgrade (build on top of previous case)</span></p>
<p>Pig -</p>
<p>Module 01. Pig – Basic Concepts and comparison with Hive</p>
<p><span style="font-size: 11px;">Module 02. Pig – Programming language</span></p>
<p><span style="font-size: 11px;">Module 03 Pig – Programming language (Continuation)</span></p>
<p><span style="font-size: 11px;">Module 04 Pig – Reading date from Hive Tables</span></p>
<p><span style="font-size: 11px;">Module 04 Pig – Ad Hoc data analytics with Pig</span></p>
<p><span style="font-size: 11px;">Module 05 Hive - Re-implementing Data pipeline using pig</span></p>
<p> </p>
