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Big Data And Hadoop – Course Contents

Big data hadoop training Courses

We at IIHT always believe in catering to the latest demands of IT industry. To match and exceed their expectations, we have Big Data and Hadoop as an offering where we train you on the below technologies:

Big Data And Hadoop Course Contents

Java Fundamentals

Java is a high-level programming language originally developed by Sun Microsystems and released in 1995. Java runs on a variety of platforms, such as Windows, Mac OS, and various versions of UNIX. This module will take you through simple and practical approach while learning Java Programming language. It consists of the essentials that a candidate should know to begin learning about Hadoop.

Hadoop Fundamentals

Hadoop is indispensable when it comes to processing big data! This module is your introduction to Hadoop Architecture, its file system (HDFS), its processing engine (MapReduce), and many libraries and programming tools associated with Hadoop.


The Hadoop Distributed File System (HDFS) is the primary storage system used by Hadoop applications. HDFS is a distributed file system that provides high-performance access to data across Hadoop clusters. Like other Hadoop-related technologies, HDFS has become a key tool for managing pools of big data. HDFS is built to support applications with large data sets, including individual files that reach into terabytes.


MapReduce is a core component of the Apache Hadoop software framework. Hadoop enables resilient, distributed processing of massive unstructured data sets across commodity computer clusters, in which each node of the cluster includes its own storage. MapReduce serves two essential functions: It parcels out work to various nodes within the cluster or map, and it organizes and reduces the results from each node into a cohesive answer to a query.


A new name has entered many of the conversations around big data recently. Some see the popular newcomer Apache Spark as a more accessible and more powerful replacement for Hadoop. Others recognize Spark as a powerful complement to Hadoop and other more established technologies, with its own set of strengths, quirks and limitations. Spark, like other big data tools, is powerful, capable, and well-suited to tackling a range of data challenges.


Apache Hive is an open-source data warehouse system built on Hadoop for querying and analyzing large datasets stored in Hadoop files. Hadoop is a framework for managing large datasets in a distributed computing environment and Hive helps in indexing, metadata storage, built-in user defined functions and more.


Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. Pig’s language layer currently consists of a textual language called Pig Latin.


HBase is an open source, non-relational, distributed database modeled after Google’s BigTable and written in Java. It is developed as part of Apache Software Foundation’s Apache Hadoop project and runs on top of HDFS (Hadoop Distributed Filesystem), providing BigTable-like capabilities for Hadoop. It provides a fault-tolerant way of storing large quantities of sparse data.


Sqoop is a tool designed to transfer data between Hadoop and relational database servers. It is used to import data from relational databases such as MySQL, Oracle to Hadoop HDFS, and export from Hadoop file system to relational databases.


Apache Hadoop YARN (Yet Another Resource Negotiator) is a cluster management technology. YARN is one of the key features in the second-generation Hadoop 2 version of the Apache Software Foundation’s open source distributed processing framework. Originally described by Apache as a redesigned resource manager, YARN is now characterized as a large-scale, distributed operating system for big data applications.


MongoDB is an open source database that uses a document-oriented data model. MongoDB is one of several database types to arise in the mid-2000s under the NoSQL banner. Instead of using tables and rows as in relational databases, MongoDB is built on an architecture of collections and documents. Documents comprise sets of key-value pairs and are the basic unit of data in MongoDB. Collections contain sets of documents and function as the equivalent of relational database tables.

Hadoop Security

Security is a top agenda item and represents critical requirements for Hadoop projects. Over the years, Hadoop has evolved to address key concerns regarding authentication, authorization, accounting, and data protection natively within a cluster and there are many secure Hadoop clusters in production. Hadoop is being used securely and successfully today in sensitive financial services applications, private healthcare initiatives and in a range of other security-sensitive environments.

So, if you looking for an IT training institute that will make you job-ready in Hadoop, then IIHT’s Engineering Programme in Big Data and Hadoop is the right choice for you!Enroll to know about Fee structure and Course Syllabus.


Q. Can non-IT candidates enroll for the Big Data and Hadoop Programme?

No. As this is an Engineering programme, a candidate first either needs to enroll for Java Programme in order to go for Android App Development.

Q. Why it is important to learn HDFS and MapReduce in Big Data and Hadoop?

HDFS and MapReduce are two major components of Hadoop architecture. These two are the bases on which the entire Hadoop ecosystem is set-up. Thus, it is very important to grasp these two topics in order to become a Hadoop Developer.

Q. Why students are required to learn ‘Spark’?

Spark has huge demand in the job market. It can get well integrated with Hadoop and is considered as an important tool to handle Big Data. Several companies have already started using Spark for processing Big Data.

Q. Why it is necessary to learn ‘Hive’, ‘HBase’, ‘Sqoop’ ‘Pig’ in order to process big data?

All these are important tools that can be easily integrated with Hadoop and help in faster processing of Big Data. Thus, it is very important for a student to grasp these tools, in order to become a professional Hadoop Developer.

Q. What is the significance of learning YARN?

YARN is the newer version of Hadoop. It is extremely important for a Hadoop Developer to get an in-depth knowledge about YARN as companies are quickly moving forward with YARN.

Q. Why MongoDB is part of the course curriculum?

Several enterprise are adopting MongoDB because of its powerful features like document oriented storage, high availability, quick updates, rich queries and so on. Thus, MongoDB becomes an inevitable part of the Big Data and Hadoop curriculum at IIHT.

Q. Is it important for students to learn about Hadoop security?

It is not enough to learn how Hadoop works or how different tools are integrated with Hadoop, it is equally important for a Hadoop Developer to learn how to keep the Hadoop cluster secure. A Hadoop developer who is able to demonstrate Hadoop Security skills to potential employers, stand a better chance of getting a job as compared to others.