Big Data

Using a network of many computers to address issues involving enormous volumes of data and computing is made possible by the Apache Hadoop suite of open-source software tools. It offers a software framework for the MapReduce programming approach for big data processing and distributed storage.

Hadoop utilises the MapReduce framework, which provides the benefit of scalability.

Hadoop features:

• Hadoop is a model that is very scalable. A cluster of affordable machines processes a huge amount of data in parallel by dividing it among them. The number of these machines, or nodes, can be changed depending on the needs of the business.
• Hadoop offers data availability even if one of your systems crashes by replicating data across several DataNodes in a Hadoop cluster. If there is a technical problem with one machine, you can still read all the data from that one machine. Because the data is copied or replicated by default in a Hadoop cluster, it may also be read from other nodes.
• Hadoop is made to work well with any type of dataset, including unstructured (images and videos), semi-structured (XML, JSON), and structured (MySQL data). This makes it extremely flexible because it can process any type of data with ease, regardless of its form.
• Data locality is a concept that is used to speed up Hadoop processing. The computer logic is moved close to the data in the data locality idea rather than the other way around.
• HDFS is the distributed file system used by Hadoop to manage its storage (Hadoop Distributed File System). A huge file is divided into smaller file blocks and distributed among the Hadoop cluster's nodes using the Distributed File System (DFS). Because so many file blocks are handled in parallel, Hadoop is faster than traditional database management systems and offers higher performance.

IOTASCALE has the expertise in developing the products using Hadoop Distributed File System