big data hadoop training in hyderabad

Hadoop training in Hyderabad


Big Data Hadoop Training in Hyderabad is the latest buzzword which is evolving in this IT world. As the technology has processed on an immense scale and this creates the need for more IT employees to take equal measures with technical progression. In this circumstance, Big Data Analytics deserves a boast which can lead to better job opportunities in the IT field today

Hadoop is an open-source software framework that supports data-intensive distributed applications licensed under the Apache v2 license. It supports the parallel running of applications on large clusters of commodity hardware. Hadoop derives from Google's Map Reduce and Google File System (GFS) papers.

The Hadoop framework transparently provides both reliability and data motion to applications. Hadoop implements a computational paradigm named MapReduce, where the application is divided into many small fragments of work, each of which can execute or re-executed on any node in the cluster. In addition, it provides a distributed file system that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. Both map/reduce and the distributed file system are designed so that node failures are automatically handled by the framework. It enables applications to work with thousands of computation-independent computers and petabytes of data. The entire Apache Hadoop platform is now commonly considered to consist of the Hadoop kernel, MapReduce and Hadoop Distributed File System (HDFS), as well as a number of related projects including Apache Hive, Apache HBase, and others.

Hadoop Training in Hyderabad 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.


1-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.
2-Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
3-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.
5-Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
6-Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.