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Big Data Hadoop Course Content (Apart from this course content we have included Linux + Python for Hadoop Mapreduce during Summer training free with Hadoop course)
Module 1:Understanding Big Data and Hadoop –week-1
Learning Objectives - In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, how MapReduce Framework works.
Learning Objectives - In this module, you will learn the Hadoop Cluster Architecture, Important Configuration files in a Hadoop Cluster, Data Loading Techniques, how to setup single node and multi node Hadoop cluster.
Learning Objectives - In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets.
Learning Objectives - In this module, you will learn Advanced MapReduce concepts such as Counters Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.
Learning Objectives - In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, PIG running modes, PIG UDF, Pig Streaming, Testing PIG Scripts. Demo on healthcare dataset.
Learning Objectives - This module will help you in understanding Hive concepts, Hive Data types, loading and Querying Data in Hive, running hive scripts and Hive UDF.
Learning Objectives - In this module, you will understand Advanced Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, optimizations in hive. You will also acquire in-depth knowledge of HBase, HBase Architecture, running modes and its components.
Learning Objectives - This module will cover Advanced HBase concepts. We will see demos on Bulk Loading, Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper.
HBase Data Model HBase Shell HBase Client API
Data Loading Techniques ZooKeeper Data Model Zookeeper Service Zookeeper
Demos on Bulk Loading Getting and Inserting Data Filters in HBase
Learning Objectives - In this module you will learn Spark ecosystem and its components, how Scala is used in Spark, SparkContext. You will learn how to work in RDD in Spark. Demo will be there on running application on Spark Cluster, Comparing performance of MapReduce and Spark.
Learning Objectives - In this module, you will understand working of multiple Hadoop ecosystem components together in a Hadoop implementation to solve Big Data problems. We will discuss multiple data sets and specifications of the project. This module will also cover Flume & Sqoop data loading Techniques, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.
Towards the end of the course, you will be working on a live project where you will be using PIG, HIVE, HBase and MapReduce to perform Big Data analytics.
Also provide working concepts with Devops tools like Git, Chef, Docker,
Project will be like: