Apache Hadoop is an open-source software framework used for distributed storage and processing of dataset of big datausing the MapReduce programming model. It consists of computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.

The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality,[3] where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking.

The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts. Though MapReduce Java code is common, any programming language can be used with "Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program.] Other projects in the Hadoop ecosystem expose richer user interfaces.

According to its co-founders, Doug Cutting and Mike Cafarella, the genesis of Hadoop was the "Google File System" paper that was published in October 2003. This paper spawned another one from Google – "MapReduce: Simplified Data Processing on Large Clusters". Development started on the Apache Nutch project, but was moved to the new Hadoop subproject in January 2006. Doug Cutting, who was working at Yahoo! at the time, named it after his son's toy elephant. The initial code that was factored out of Nutch consisted of about 5,000 lines of code for HDFS and about 6,000 lines of code for MapReduce..

The first committer to add to the Hadoop project was Owen O'Malley (in March 2006); Hadoop 0.1.0 was released in April 2006. It continues to evolve through the many contributions that are being made to the project.

What is hadoop?

Hadoop is an open source framework from Apache and is used to store process and analyze data which are very huge in volume. Hadoop is written in Java and is not OLAP (online analytical processing). It is used for batch/offline processing.It is being used by Facebook, Yahoo, Google, Twitter, LinkedIn and many more. Moreover it can be scaled up just by adding nodes in the cluster..

Benefits of doing this course

• Enhance your Market Value.
• Freelance Support to Earn Money.
• You are able to Identify which channel of hadoop to use effectively for a business.
• Work on the components of digital marketing to create a complete experience.
• Work on understanding and identifying your audience broadly.
• Make informed decisions about hadoop campaigns

Courses Covered

• Introduction to Big Data , Hadoop Architecture and HDFS, Hadoop Clusters and the Hadoop Ecosystem, Hadoop MapReduce Framework, MapReduce programs in Java, Hive and HiveQL .
• PIG , Apache SQOOP, Flume, Dependency Injection & Services, NoSQL Databases, Oozie and Zookeeper , Testing,Project

Benefits of hadoop?

• Existing architecture employs Schema-onWrite(RDBMS) model
• Create Schema
• Transform data so as to fits it into this schema
• Load data
• Obtain required information
• Efficient for compression, indexing,partitioning but difficult to adapt to changes.

Who should take this Course

• Students
• Working Professionals
• Entrepreneurs

Course Duration

The Duration of this course is 12 Months

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Job Opportunities After This Course

        Hadoop Administrator
        Technical Lead - Hadoop
        Hadoop - Senior Data Engineer
        Hadoop / Big Data Developer
        Cloudera (Hadoop) Administrator
        Hadoop Admin
        Hadoop Developer (Entry-Level)
        Big Data/Hadoop Developer
        Sr Hadoop Developer
        Data Warehouse Developer (Hadoop)
        Senior Database Specialist - SQL.
         Sr. Software Developer - Hadoop
        Senior Administrator
         Jr. Hadoop Developer
        SR Hadoop Administrator

Advantage of Hadoop

         Scalable
        Cost effective
         Flexible
         Fast
        Resilient to failure
         HDFS store large amount of information
         HDFS is simple and robust coherency model
         Data will be written to the HDFS once and then read several times.
        Ability to rapidly process large amounts of data in parallel.

Why Join Us

        100% Practical and hands on Learning

        Study Notes

        Special Batch for Working Professionals

        Experimental Learning through Live Projects.

        Resume & Interviews Preparation Support

        Working Professionals

        Flexible batches

        Well-structured and industry-relevant course curriculum

        The best Institute in Bangalore