The format is a very important feature of any dataset. There are lots of formats which are used in different different contexts for a certain kind of process. Each format has its own benefits. Let us formats with an example of images. Image formats can be in the form of JPEG, PNG, and GIF. Here, each format has a specific attribute and is used for different purposes. Just like this, any data processing set will need an input file which has to be in a format that is better read by the system. That is why any data file will be having an extension when we download it. It all depends upon the input file which we are feeding into the system.

For big data processing tools like Hadoop and hive, there are a lot of formats which are used for different kind of usages. Let us specifically talk about Hadoop formats or the HDFS file format which expands as a Hadoop distributed file system which is specifically used to run a commodity software. We are specific about a kind of format because of the benefits we can reap out of it. Some of the advantages of the formats are 1) a better call function which makes it easy for the system to find file in the appropriate location, 2) better write function which is the post-call functionality which will help in better processing of the called file, 3) easily edit and format conversion is very ideal as any data set will be subjected to a lot of changes over the data processing time, 4) easily row to column conversions and compressions will help in better storage.

Let us talk about some of the prominent names in the industry like Hive ORC which expands to hive optimized row columnar format is a type of formatting which is predominantly used in the industry and is also a widely accepted one due to the accuracy and the flexibility the formatting has to offer to us.  These use the Hive map features like MapReduce for the conversion and compression of the data set. People who are handling data with Hadoop and other tools will be very much familiar with MapReduce. It is a tool for solving general and common data manipulation problems. As most people think, MapReduce is not a tool by a framework for problem-solving which uses patterns to arrive at a specific problem. This why MapReduce Design Patterns are more sought by developers in complex problem-solving. Patterns are very much helpful in complex problem-solving. They make life easier. MapReduce uses the simple strategy of find solutions to a problem by mapping and reduces mechanism which is also the best way to approach any logical problem. The reusable nature of the framework does more advantage than we can imagine. They provide a regulatory and also a general framework which is applicable for a lot of problems irrespective of the size and nature. The approach is more important than the problem itself.