MapReduce is the key to perform processing on Big Data, but it is complex to understand, design, code, and optimize. MapReduce has a high learning curve, which requires good programming skills to master. Usually Big Data users come from different backgrounds such as Programming, Database administrators, scripting, Analyst, Data science, Data Managers, and so on, and not all users can adapt to the programming model of MapReduce. Hence we have different abstractions for the data access components for Hadoop.
The data access components are very useful for developers as they may not need to learn MapReduce programming in detail and can still utilize the MapReduce framework in an interface in which they can be much more comfortable and can help in faster development and better manageability of the code. Abstractions can help ad hoc processing on data quickly and concentrate on the business logic.
The two widely used data access components in the Hadoop ecosystem...