Big Data/Hadoop Course Outline

Training mode : We offer face to face class room training , Online and Fasttrack training programs

This course has been targeted for Architects, Administrators and developers

Attend Once and Play any role as  you wish !

Module 1 Big data Getting Started	What is Big Data? What  is Apache Hadoop ? History of Hadoop Understanding distributed file systems and Hadoop Hadoop eco system components Hadoop use cases Ubuntu Installation JDK Installation
Module 2 Hadoop Distributed File system	Hadoop V1 & V2 Architecture  Eclipse Installation Overview of HDFS Communication Protocols Rack Awareness Hadoop cluster Topology Setting up SSH for Hadoop Cluster Running Hadoop – 1.       Pseudo-distributed mode Linux basic commands HDFS file commands Reading and writing to HDFS programmatically Hands-on Lab Exercises
Module 3 MapReduce Framework	Java Basics Anatomy of a MapReduce Program Writables InputFormat OutputFormat Streaming API Inherent failure handling Reading and writing Hands-on Lab Exercises
Module 4 Advanced MapReduce  Programming	Input splits, Record Reader, Mapper, Partition & Shuffle, Reduce, OutputFormat Writing MapReduce program Streaming in Hadoop Counters Performance Tuning Joins Sorting Determining Optimal number of reducers, partitions Hadoop cluster – Performance tuning Hands-on Lab Exercises
Module  5 Apache Hadoop Administration	Best Practices for Hadoop setup and infrastructure Hadoop cluster Installation preparation    Ø  Cluster network design    Ø  Installation of Linux operating system    Ø  Configuring SSH    Ø  Walkthrough on Rack topology and set up Managing Hadoop cluster    Ø  HDFS cluster management    Ø  Secondary Name node configuration    Ø  Task Tracker management    Ø  Configuring the HDFS quota    Ø  Configuring Fair Scheduler          Ø  Upgrading Hadoop         Ø  Deploying and managing Hadoop clusters           with Ambari Monitoring Hadoop cluster    Ø  Monitoring Hadoop cluster with Ganglia    Ø  Monitoring Hadoop cluster with Ambari    Ø  Monitoring Hadoop cluster with Nagia Hadoop Cluster Performance Tuning    Ø  Benchmarking and profiling    Ø  Using compression for input and output    Ø  Configuring optimal map and reduce           slots  for the TT    Ø  Fine tuning Job Tracker config    Ø  Fine tuning Task Tracker config    Ø  Tuning Shuffle, merge and sort parameters     Security Implementation               Kerberos security Implementation       Workflow Scheduler               Capacity Scheduler                Fair Scheduler   dfsadmin & mradmin commands Administration of Hcatalog and Hive Backup and Recovery Scenario based exercises -          Data node failure & Recovery -          Name Node Failure & Recovery -          JT & TT failure  & Recovery -          Removing data nodes -          Adding Data nodes -          Commissioning and decommissioning of nodes
Module  6 Pig and Pig Latin	Installation and configuration Running Pig Lating through grunt Writing programs -          Filter , Load & Store functions Writing user defined functions Working with Scripts Lab Exercises
Module  7 HBase and ZooKeeper	NoSQL Vs SQL Cap  Theorem Architecture Installation Configuration Java API MR integration Performance Tuning Lab Exercises
Module  8 Hive	Features of Hive Architecture Installation and configuration HiveQL Lab Exercises
Module  9 Other Hadoop eco system components Apache Spark and eco system	Overview of Ambari, Oozie ,Mahout Installing & configuring Sqoop, mysql-server Installing & configuring flume Scala Environment setup REPL Control Statements Variables and expressions  Classes, Objects, Traits, Types and Methods First Class Functions, Higher order functions, Procedures Closures Currying Working with SBT  Introduction to Spark and its EcosystemSpark Overview Spark Installation & Ecosystem walkthrough  Running a sample Spark Architecture and Programming with RDDs Spark Architecture Spark Shell Spark Context RDD Introduction Basic programming with RDDs Common Transformation and Actions Working with Spark jobs Parquet  Data model  Parquet File Format  Writing and Reading parquet files Analytics with Spark SQL Spark SQL Basics. Create tables and working with data  Advanced Sqark SQL Queries DataFrames/Schema RDDs Loading and Saving data from/to RDD, Hive, Databases Performance of MapReduce Vs Spark Advanced Programming Persistence and Caching Accumulators and Broadcast Variables Pair and Numeric RDD Operations Pre-partition Working with various file formats and file systems RDD Partitioning Running Spark in Cluster Tuning and Debugging Lab Exercises
Module 10 Hadoop on Cloud	Hosting Hadoop on Amazon EC2 EMR Hands-on

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