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Hadoop is a solution of big data. It provides support for many of the tools in the Hadoop ecosystem including Hive, H Base, Pig, Sqoop and Zookeeper. Hadoop is an open-source software framework used for distributed storage and processing of dataset of big data using the Map Reduce programming model.

We offer you the opportunity to master your skills in Hadoop and spark and other Courses training in PUNE, AUNDH such as MapReduce, Hive, Pig, HBase, Flume, Apache Spark and AWS many others. After completion of the certification, you will be able to show your skills in some sectors such as IT, data management, finance and many others.

Technogeeks also provide FREE Technical Seminar on Hadoop every Saturday, will also provide some brief Idea about Hadoop and Its components so that if you are planning to learn Hadoop, then you can get some brief idea before that.

Many jobs are coming in Hadoop and Big Data industries as per its market demand. So this is the right time to choose a career in Big Data Hadoop. Technogeeks is offering Hadoop Big Data Classes in Pune, you may also contact Technogeeks for Hadoop and Big Data training, call at +91-860-099-8107 or mail at contact@technogeekscs.co.in.

Hadoop training will give you so many career options to perform well, to earn well! And for that "TECHNOGEEKS" is here to help you to start with in Pune Location in India. So, you can visit and attend Free Seminar on weekends as they usually provide Free Seminar on Hadoop Training in Pune by IT working professionals every weekend.

Hadoop is an open source framework of Apache which is used to store process and analyze data in very big volume. Hadoop is leading Big Data platform used by top IT organizations like Facebook, Google, Yahoo and many more.

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.