Big Data Hadoop

Hadoop – Big Data Development & Analytics – V2

BigData Hadoop Server

!! Apache Community Delivers Big Data Solution to Advance Enterprise Analytics !!

The data revolution is upon us and Hadoop is THE leading Big Data platform. Fortune 500 companies are using it for storing and analyzing extremely large datasets, while other companies are realizing its potential and preparing their budgets for future Big Data positions. It’s the elephant in Big Data’s room!

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This series will get you up to speed on Big Data and Hadoop. Topics include how to install, configure and manage a single and multi-node Hadoop cluster, configure and manage HDFS, write MapReduce jobs and work with many of the projects around Hadoop such as Pig, Hive, HBase, Sqoop, and Zookeeper. Topics also include configuring Hadoop in the cloud and troubleshooting a multi-node Hadoop cluster.

The Apache Hadoop software is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Hadoop is a software framework for storing and processing Big Data. It is an open-source tool build on java platform and focuses on improved performance in terms of data processing on clusters of commodity hardware.

  • Hadoop comprises of multiple concepts and modules like HDFS, Map-Reduce, HBASE, PIG, HIVE, SQOOP and ZOOKEEPER to perform the easy and fast processing of huge data
  • Hadoop conceptually different from Relational databases and can process the high volume, high velocity and high variety of data to generate value

After the completion of the Big Data and Hadoop Course, you should be able to:

  • Master the concepts of Hadoop Distributed File System and MapReduce framework
  • Setup a Hadoop Cluster
  • Understand Data Loading Techniques using Sqoop and Flume
  • Program in MapReduce (Both MRv1 and MRv2)
  • Learn to write Complex MapReduce programs
  • Program in YARN (MRv2)
  • Perform Data Analytics using Pig and Hive
  • Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing
  • Have a good understanding of ZooKeeper service
  • New features in Hadoop 2.0 — YARN, HDFS Federation, NameNode High Availability
  • Implement best Practices for Hadoop Development and Debugging
  • Implement a Hadoop Project
  • Work on a Real Life Project on Big Data Analytics and gain Hands on Project Experience

Hiring IT professionals who are certified as Hadoop BigData allows many organizations to increase their ratio of servers to administrators, enabling them to be more cost effective in building out their infrastructures without needing to bring on additional resources.


– Why Hadoop, Scaling, Distributed Framework, Hadoop v/s RDBMS, Brief history of Hadoop, Problems with traditional large-scale systems, Requirements for a new approach, Anatomy of a Hadoop cluster, Other Hadoop Ecosystem components

Hadoop Architecture
– 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
– Pseudo mode, Cluster mode, Installation of Java, Hadoop, Configurations of Hadoop, Hadoop Processes ( NN, SNN, JT, DN, TT), Temporary directory, UI, Common errors when running Hadoop cluster, Solutions, 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
– 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.
Advance MapReduce
– 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
– 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.
Hive and HiveQL
– Hive Architecture and Installation, Comparison with Traditional Database, HiveQL: Data Types, Operators and Functions, Hive Tables(Managed Tables and External Tables, Partitions and Buckets, Storage Formats, Importing Data, Altering Tables, Dropping Tables), Querying Data (Sorting And Aggregating, Map Reduce Scripts, Joins & Subqueries, Views, Map and Reduce side Joins to optimize Query).
Advance Hive, NoSQL Databases and HBase
– 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
  – HBase: Advanced Usage, Schema Design, Advance Indexing, Coprocessors, Hadoop Project: HBase tables The ZooKeeper Service: Data Model, Operations, Implementation, Consistency, Sessions, States.
Hadoop 2.0, MRv2 and YARN
– Schedulers:Fair and Capacity, Hadoop 2.0 New Features: NameNode High Availability, HDFS Federation, MRv2, YARN, Running MRv1 in YARN, Upgrade your existing MRv1 code to MRv2, Programming in YARN framework.
Hadoop Project Environment and Apache Oozie
  – In this module, you will understand how multiple Hadoop ecosystem components work 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 Apache Oozie Workflow Scheduler for Hadoop Jobs.