Hadoop is an open-source framework that supports distributed processing of large data sets across clusters of computers using simple programming models.

It is made to scale from a few servers to thousands of machines with local storage on each one. It is typically used to support machine learning and other types of data-intensive applications that demand very fast throughput at low latency.

In Hadoop, the entire file system resides on the cluster’s shared storage, making it more difficult to get faster access to a subset of the file system. MapReduce combines parallel map operations and reduces operations into a pipeline that operates on keys and values, not files or blocks. It automatically schedules maps and reduces tasks onto nodes where data can be read or written locally for faster execution. Mappers are responsible for generating key-value pairs from input data (such as sorting), while reducers take these key-value pairs as input, aggregating all records with the same key together. The shuffle operation sorts and merges these records prior to writing them out to the output topic.

Ficusoft provides certified Big Data Professionals with hands-on training on Hadoop Distributed File System, Hadoop Clusters, Hadoop MapReduce, and its Big Data Processing Ecosystem through their workshop and live project training classes . They also offer demo free class sessions to help you understand the course better before enrolling yourself. For example, they have short workshops covering topics like Introduction to HDFS Architecture and Administration, Introduction to Pig Scripts, HBase Introduction: Design and Implementation etc. So don’t wait any longer! Join our workshop today!