Handling big data with Hadoop toolkit

The digital age has allowed us to work remotely but collaboratively, generate and move terabytes of data at levels that was never anticipated 25 years ago and it has allowed us to explore the details and complex workings of nearly every part of our physical world. But our ability to process all this information is out pacing our infrastructure and is requiring us to develop new analytical methods and techniques that can fully exploit these datasets. Storing, securing and reconciling data are the most fundamental aspects of any data management strategy. Companies struggle with it for many reasons. The most common obstacle for companies is that they have too much data and too few resources. Of course, the solution is to either collect fewer data or to invest for handling and managing the big data by finding out the right tools and technologies to efficiently gain valuable in sights that lift up profit in their businesses. This, in a nutshell, is what Hadoop provides: a reliable shared storage and analysis system. The storage is provided by HDFS and analysis by MapReduce. There also other parts of Hadoop eco system in which some of those that are familiar and most used by many enterprises and organizations are noted and discussed. These are not comprehensive lists and the story of big data is still being written and new methods and tools continue to be developed to solve new problems.