Big data is also like any other IT project which pull the information and reports to management for better decision, competitor analysis, Marketing Strategies etc., for business administration. The major difference is Big data is an Information pool which is combination of Structured, Semi Structured and Unstructured data made up of large data sets that cannot be processed using traditional methods of computing.
Big Data Application Testing Stages
There are several areas in the process / workflow of a big data project where testing required. Testing Process in big data projects is typically related to
1) Database Testing
2) Infrastructure Testing
3) Performance testing
4) Functional testing
Having a clear test strategy contributes to the success of the project.
Big data is data is pool from ERP data, IoT, IIoT device, Embedded Systems, Log files, Emails, Images or Audio or Videos or Social Media Data etc., it is a combination of different Data formats in big data can be classified into three categories. They are:
a) Structured Data
b) Semi Structured Data
c) Unstructured data
Testing of all this different formats, in combination majorly Big data Tester is different from a traditional Tester, need complete understanding Project inputs of different datatypes which is not like RDBMS Data and he need to understand what data is coming to pool, first tester need to understand all this environment about project before start the testing process, what is intention of the usage for, how they want to display the data .
Why require Big Data Testing ?
Why we are analysing the data is, to get information which leads to better decision, if we not tested the process before properly it would affect the business significantly.
There is different stages in Big Data Project life cycle. On every stage the testing is mandatory for proper out of the reports or analytics, for processing huge amount of data and getting results to use for management decisions.
High availability and accuracy of the Analytics is leading to a demand of big data testing tools, techniques and frameworks.
Testing In each stage is required as increased data leads to an increased risk of errors and thus, to show the performance of applications and software. In all stages Big Data Functional & Performance testing becomes very much important.To test the big data system which usage of appropriate data It is very much important to get MIS (MIS stands for management information system) Reports.
Big Data Testing can be broadly divided into three stages : -
1) Data Staging Validation
2) Map Reduce Validation
3) Output Validation
Stage 1: Data Staging Validation
The first step of big data testing, also referred as pre-Hadoop stage involves process validation.
Stage 2: “MapReduce” Validation
The second step is a validation of “MapReduce”. In this stage, the tester verifies the business logic validation on every node and then validating them after running against multiple nodes, ensuring that the process works correctly.
Stage 3: Output Validation Phase
The final or third stage of Big Data testing is the output validation process. The output data files are generated and ready to be moved to an EDW (Enterprise Data Warehouse) or any other system based on the requirement.
Benefits of Using Big Data Testing : -
Through Big Data testing, you can ensure the data in hand is qualitative, accurate and healthy. The data you had collected from different sources and channels are validated, aiding in better decision making. There are several benefits to Big Data testing.
Better Decision Making — When data gets in the hands of the right people, it becomes an asset. So when you have the right kind of data in hand, it would help you make sound decisions. It lets you analyze all the risks and make use of only the data that will contribute to the decision making process.
Data Accuracy — Gartner says that data volume is likely to expand by 800% in the next 5 years, and 80% of this data will be unstructured. Imagine the volume of data that you have to analyze. You need to convert all this data into a structured format before it can be mined. Armed with the right kind of data, businesses can focus on their weak areas, and be better prepared to beat the competition.
Better Strategy and Enhanced Market Goals — You can chart a better decision-making strategy or automate the decision-making process with the help of big data. Collect all the validated data, analyze it, understand user behavior and ensure all of them are realized in the software testing process, so you can deal out something they need. Big data testing helps you optimize business strategies by looking at this information.
Increased Profit and Reduced Loss — Loss in business will be minimal or even a thing of past, if data is correctly analyzed. If the accumulated data is of poor quality, the business suffers terrible losses. Isolate valuable data from structured and semi-structured information so no mistakes are made when dealing with customers.
Big Data processing is a very promising field in today’s complex business environment. Applying the right dose of test strategies, and following best practices would help ensure qualitative software testing. The idea is to recognize and identify the defects in the early stages of testing and rectify them. This helps in cost reduction and better realization of company goals. Through this process, the problems that testers faced during software testing are all solved now because the testing approaches are all driven by data.