A huge set of complicated structured and unstructured data is called as Big Data. When we come across testing of Big Data, a lot of processes and techniques are involved.

Big Data testing is a proof  of the perfect data  dealing, instead of testing the tool. In testing of data, Performance and functional testing are the keys. Since the working is quick, so testing of this technology has to be maintained with high standard. In testing data, the data value also needs to be taken cared of.

Signs That Show We Should Go For Testing Are

1. Presentation Testing: in view of the fact that Big Data applications work together with existing statistics for genuine occasion analytics, so in that concern presentation is the solution. Presentation testing, like any other testing procedure, makes the procedure keep going.
2. Problems With Expansion Capacity: The Big data handles a huge set of data and stores them safely and in properly arranged manner. It starts with lesser sets of statistics and ends up with an overweight quantity of statistics.
3. Towering Quantity Of Downtime: During high analytic issues of Big data, due to a large number of problems, the data faces certain issues resulting in a reduction of downtime. So if a continuous amount of downtimes occur, then users should be a concern and be sure that it is time for testing the Big Data Analytics.
4. Poor Improvement: Data management is a must in running a proper organization or any small or bigger business. Failure in handling data efficiently for longer time span would result in improper development. Hence for running the business appropriately, proper testing of data is required, because the delivery of the proper result  to clients
5. No Proper Control: Require proper control of the information the business work with. And this proper data can be obtained only by frequently checking the data.
6. Poor Safety Measures: Since big data stores the organization’s complete data from credential sets to all the confidential reports so safety and protection in Big data is a must and the management have to make sure that the data stored in HDFS of big data is secured to the fullest.
7. Problems With The Proper Running Of The Applications: For performing various applications, the Big Data collects information from various sources. These data seems to be not too easy to analyze. Before applying those data to be used in different applications they should undergo a testing procedure to find out if they are fit for the analysis. The quality of the information used in the applications will determine the quality of those applications too.
8. Proper Output: In order to get the best output in any project proper input is necessary and correction and testing of input must be made sure to determine the best output ever.

The Testing Procedure Is Filled With

1. Data Phase Proofing
2. Proofing of MapReduce
3. Proofing of the Output

Performance Testing Approach

Performance testing for big data application involves testing of huge volumes of planned and shapeless data, and it requires a specific testing approach to test such massive data.

Hadoop is involved with storage and maintenance of a large  set of data including both structured as well as unstructured data . A large and long flow of testing procedure is included here.

• First of all do the set up of the application prior to the testing procedure begins.
• Find out the required workloads and make the design accordingly
• Make ready each and every client separately
• Perform the testing procedure and also check the output carefully
• Do the best possible organization

Test Atmosphere Requirements

• As always Hadoop structure should be more  spacious since it has to process a large set of data.
• The cluster should contain a large set of nodes to handle the stored information.
• The CPU should be utilized properly.