Hadoop Big Data Training 

Introduction to Bigdata and Hadoop

• Introduction to big data
• limitations of existing solutions
• Hadoop architecture
• Hadoop components and ecosystem
• data loading & reading from HDFS
• replication rules
• rack awareness theory
• Hadoop cluster administrator: Roles and responsibilities 

Hadoop Architecture and cluster setup

• Hadoop server roles and their usage
• Hadoop installation and initial configuration
• Deploying Hadoop in a pseudo-distributed mode
• Deploying a multi-node Hadoop cluster
• Understanding the working of HDFS and resolving simulated problems 

Hadoop Cluster Administration and understanding Mapreduce

• Understanding secondary namenode
• Working with Hadoop distributed cluster
• Decommissioning or commissioning of nodes
• Understanding MapReduce 

Backup, Recovery and Maintenance

• Key Hadoop Admin Commands
• Trash
• Import Check Point
• Distcp, data backup, and recovery
• Enabling trash
• Namespace count quota or space quota
• Manual failover or metadata recovery 

Capacity Planning and Management

• Planning a Hadoop 2.0 cluster
• Cluster sizing, hardware
• Network and software considerations
• Popular Hadoop distributions
• Workload and usage patterns 

Hadoop 2.0 Features

• Limitations of Hadoop 1.x
• Features of Hadoop 2.0
• YARN framework
• Hadoop high availability and federation
• YARN ecosystem and Hadoop 2.0 Cluster setup 

Setting up Hadoop 2.X with High Availability and upgrading Hadoop

• Configuring Hadoop 2 with high availability
• upgrading to Hadoop 2
• working with Sqoop
• understanding Oozie
• working with Hive
• working with HBase 

Cloudera manager and Cluster setup, Overview on Kerberos

• Hive administration
• HBase architecture
• HBase setup, Hadoop/Hive/HBase performance optimization
• Pig setup and working with a grunt, why Kerberos and how it helps

 Understanding Bigdata and Hadoop

• Big Data, Limitations and Solutions of existing Data Analytics Architecture,
• Hadoop, Hadoop Features,
• Hadoop Ecosystem, Hadoop 2.x core components,
• Hadoop Storage: HDFS, Hadoop Processing:
• MapReduce Framework,
• Hadoop Different Distributions.

Yarn

• YARN (Yet another Resource Negotiator) – Next Gen.
• Map Reduce
• What is YARN?
• Difference between Map Reduce & YARN
• YARN Architecture
• Resource Manager
• Application Master
• Node Manager

Hadoop Architecture and HDFS

• Hadoop 2.x Cluster Architecture –
• Federation and High Availability,
• A Typical Production Hadoop Cluster,
• Hadoop Cluster Modes,
• Common Hadoop Shell Commands,
• Hadoop 2.x Configuration Files,
• Single node cluster and Multi node cluster set up
• Hadoop Administration.

Hadoop Mapreduce Frameworks

• MapReduce Use Cases,
• Why MapReduce,
• Hadoop 2.x MapReduce Architecture,
• Hadoop 2.x MapReduce Components,
• YARN MR Application Execution Flow,
• YARN Workflow,
• Demo on MapReduce. Input Splits,
• Relation between Input Splits and HDFS Blocks,
• MapReduce: Combiner & Partitioner,
• Demo on de-identifying Health Care Data set,
• Sequence Input Format,
• Xml file Parsing using MapReduce.

Pig

• About Pig,
• MapReduce Vs Pig,
• Pig Use Cases,
• Programming Structure in Pig,
• Pig Running Modes,
• Pig components,
• Pig Execution,
• Pig Latin Program,
• Data Models in Pig,
• Pig Data Types,
• Shell and Utility Commands,
• Pig Latin : Relational Operators, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Specialized joins in Pig, Built In Functions (Eval Function, Load and Store Functions, Math function, String Function, Date Function, Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution),

Hive

• Hive Background,
• Hive Use Case,
• About Hive, Hive Vs Pig,
• Hive Architecture and Components,
• Metastore in Hive,
• Limitations of Hive,
• Comparison with Traditional Database,
• Hive Data Types and Data Models,
• Partitions and Buckets,
• Hive Tables(Managed Tables and External Tables),
• Importing Data, Querying Data,
• Managing Outputs,
• Hive Script, Hive UDF,
• Retail use case in Hive

Advanced Hive and Hbase

• Hive QL:Joining Tables,
• Dynamic Partitioning,
• Hive Indexes and views
• Hive query optimizers,
• Hive : Thrift Server,
• User Defined Functions,
• HBase: Introduction to NoSQL Databases and HBase,
• HBase v/s RDBMS,
• HBase Components,
• HBase Architecture,
• Run Modes & Configuration,
• HBase Cluster Deployment.

Advanced Hbase

• HBase Data Model,
• HBase Shell,
• Data Loading Techniques,
• ZooKeeper Data Model,
• Zookeeper Service,
• Zookeeper,
• Demos on Bulk Loading,
• Getting and Inserting Data,
• Filters in HBase.

 Sqoop

• Architecture – Installation — Commands(Import ,
• Hive-Import, EVal, Hbase Import,
• Import All tables, Export) – Connectors to Existing DBs
• Hands on Exercise

Flume 

• Flume Introduction
• Flume Architecture
• Flume Master ,
• Flume Collector and Flume Agent
• Flume Configurations
• Real Time Use Case using Apache Flume

MongoDB (As part of NoSQL Databases)

• Need of NoSQL Databases
• Relational VS Non-Relational Databases
• Introduction to MongoDB
• Features of MongoDB
• Installation of MongoDB
• Mongo DB Basic operations
• REAL Time Use Cases on Hadoop & MongoDB Use Cases

Oozie and Hadoop Project

• 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,

Spark

• Introduction to Apache Spark
• Role of Spark in Big data
• Who is using Spark
• Installation of SparkShell and StandAlone Cluster
• Configuration
• RDD Operations (Transformations and Actions)