Database Training Course

Hadoop big data training course is designed to provide knowledge & skills to become a finest Hadoop Developer & Administrator. In-depth knowledge of concepts with hands-on exercises such as Hadoop Architecture, HDFS, Map-Reduce, HBase, HIVE, PIG, Flume, Sqoop, Oozie, Spartk, BigInsights, Administering Hadoop Cluster etc., will be covered. There will be well-designed challenging, practical and focused hands-on exercises.




According to Wikipedia, Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.” In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any organization is successful in managing its data well, it can easily reach its target in very short span than the usual time. But how the organizations manage it?


  • Apache Hadoop is an open-source software framework that supportsdata-intensive distributed applications, licensed under the Apache v2 license. It supports the running of applications on large clusters of commodity hardware. Hadoop was derived from Google's Map/Reduce and Google File System (GFS) papers.
  • Hadoop is written in the Java programming language and is an Apache top-level project being built and used by a global community of contributors. Hadoop and its related projects (Hive, HBase, Zookeeper, and so on) have many contributors from across the ecosystem. Though Java code is most common, any programming language can be used with "streaming" to implement the "map" and "reduce" parts of the system.
  • Hadoop was created by Doug Cutting and Mike Cafarella in 2005. Cutting, who was working at Yahoo at the time, named it after his son's toy elephant. It was originally developed to support distribution for the Nutch search engine project.


  • Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT), that's a key consideration.
  • Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
  • Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
  • Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
  • Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.


Software Engineers, who are into ETL/Programming and exploring for great job opportunities globally.

Managers, who are looking for the latest technologies to be implemented in their organization, to meet the current & upcoming challenges of data management.

Any Graduate/Post-Graduate, who is aspiring a great career towards the cutting edge technologies.


Prerequisites for learning Hadoop include hands-on experience in Core Java and good analytical skills to grasp and apply the concepts in Hadoop. We provide a complimentary Course "Java Essentials for Hadoop" to all the participants who enroll for the Hadoop Training. This Big Data Hadoop training helps you brush up your Java Skills needed to write Map Reduce programs.

PostgreSQL Training Duration

Regular classroom based training: 4 Weeks, 60 minutes of theory & practical session per day.


Course Content

  1. Big Data
  2. 3Vs
  3. Role of Hadoop in Big data
  4. Hadoop and its ecosystem
  5. Overview of other Big Data Systems
  6. Requirements in Hadoop
  7. UseCases of Hadoop
  1. Design
  2. Architecture
  3. Data Flow
  4. CLI Commands
  5. Java API
  6. Data Flow Archives
  7. Data Integrity
  8. WebHDFS
  9. Compression
  1. Theory
  2. Data Flow (Map – Shuffle – Reduce)
  3. Programming [Mapper, Reducer, Combiner, Partitioner]
  4. Writables
  5. InputFormat
  6. Outputformat
  7. Streaming API
  1. Counters
  2. CustomInputFormat
  3. Distributed Cache
  4. Side Data Distribution
  5. Joins
  6. Sorting
  7. ToolRunner
  8. Debugging
  9. Performance Fine tuning
  1. Hardware Considerations – Tips and Tricks
  2. Schedulers
  3. Balancers
  4. NameNode Failure and Recovery
  1. NoSQL vs SQL
  2. CAP Theorem
  3. Architecture
  4. Configuration
  5. Role of Zookeeper
  6. Java Based APIs
  7. MapReduce Integration
  8. Performance Tuning
  1. Architecture
  2. Tables
  3. DDL – DML – UDF – UDAF
  4. Partitioning
  5. Bucketing
  6. Hive-Hbase Integration
  7. Hive Web Interface
  8. Hive Server
  1. Pig (Pig Latin , Programming)
  2. Sqoop (Need – Architecture ,Examples)
  3. Introduction to Components (Flume, Oozie,ambari)
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+91 9710107874
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