Learning Objectives – In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem, How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
Topics – What is Big Data, Hadoop Architecture, Hadoop ecosystem components, Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework, Hadoop Server Roles: NameNode, Secondary NameNode, and DataNode, Anatomy of File Write and Read.
Hadoop Cluster Configuration and Data Loading
Learning Objectives – In this module, you will learn the Hadoop Cluster Architecture and Setup, Important Configuration files in a Hadoop Cluster, Data Loading Techniques.
Topics – Hadoop Cluster Architecture, Hadoop Cluster Configuration files, Hadoop Cluster Modes, Multi-Node Hadoop Cluster, A Typical Production Hadoop Cluster, MapReduce Job execution, Common Hadoop Shell commands, Data Loading Techniques: FLUME, SQOOP, Hadoop Copy Commands, Hadoop Project: Data Loading.
Hadoop MapReduce framework
Learning Objectives – In this module, you will understand Hadoop MapReduce framework and how MapReduce works on data stored in HDFS. Also, you will learn what are the different types of Input and Output formats in MapReduce framework and their usage.
Topics – Hadoop Data Types, Hadoop MapReduce paradigm, Map and Reduce tasks, MapReduce Execution Framework, Partitioners and Combiners, Input Formats (Input Splits and Records, Text Input, Binary Input, Multiple Inputs), Output Formats (TextOutput, BinaryOutPut, Multiple Output), Hadoop Project: MapReduce Programming.
Learning Objectives – In this module, you will learn Advance MapReduce concepts such as Counters, Schedulers, Custom Writables, Compression, Serialization, Tuning, Error Handling, and how to deal with complex MapReduce programs.
Topics – Counters, Custom Writables, Unit Testing: JUnit and MRUnit testing framework, Error Handling, Tuning, Advance MapReduce, Hadoop Project: Advance MapReduce programming and error handling.
Pig and Pig Latin
Learning Objectives – In this module, you will learn what is Pig, in which type of use case we can use Pig, how Pig is tightly coupled with MapReduce, and Pig Latin scripting.
Topics – Installing and Running Pig, Grunt, Pig’s Data Model, Pig Latin, Developing & Testing Pig Latin Scripts, Writing Evaluation, Filter, Load & Store Functions, Hadoop Project: Pig Scripting.
Advance Hive and HBase
Learning Objectives – In this module, you will understand Advance Hive concepts such as UDF. You will also acquire in-depth knowledge of what is HBase, how you can load data into HBase and query data from HBase using client.
Topics – Hive: Data manipulation with Hive, User Defined Functions, Appending Data into existing Hive Table, Custom Map/Reduce in Hive, Hadoop Project: Hive Scripting, HBase: Introduction to HBase, Client API’s and their features, Available Client, HBase Architecture, MapReduce Integration.
Advance HBase and ZooKeeper
Learning Objectives – This module will cover Advance HBase concepts. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper and how to Build Applications with Zookeeper.
Topics – HBase: Advanced Usage, Schema Design, Advance Indexing, Coprocessors, Hadoop Project: HBase tables The ZooKeeper Service: Data Model, Operations, Implementation, Consistency, Sessions, States.
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.
Topics : What is Apache Spark, Spark Ecosystem, Spark Components, History of Spark and Spark Versions/Releases, Spark a Polyglot, What is Scala?, Why Scala?, SparkContext, RDD.
Oozie and Hadoop Project
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 demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.
Topics : Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web Console, Oozie for MapReduce, PIG, Hive, and Sqoop, Combine flow of MR, PIG, Hive in Oozie, Hadoop Project Demo, Hadoop Integration with Talend.
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