Comprehensive Guide for Big Data Automation Testing

Big data has evolved as a buzz word in the present scenario. When it comes to big data, it refers to the group of large data volumes or sets which the traditional techniques of computing are unable to process. The exponential growth of big data applications increases the demand for big data automation testing. While big data automation testing does not involve testing of the individual features, it includes the verification of the data processing. Here are some of the most important concepts that you need to know about big data automation testing.

Importance of Big Data Testing

In the present scenario, businesses are highly dependent on large volumes of data. The data obtained from various channels and sources prove to be important for the businesses for decision-making purposes. Any error in data can lead to inappropriate decision making, thereby affecting the overall performance of the business. Here the role of big data testing service comes in. The big data automation testing helps to determine whether the data is healthy, precise, as well as qualitative or not.

Some of the main reasons why big data testing is so important for businesses are:

1. Data Accuracy

In order to stay ahead in a highly competitive market, businesses require accurate data. With accurate data, businesses can find out their weak points and improve them to compete strongly. Big data automation testing helps in providing accurate data for better outcomes of the business.

2. Enhanced Decision Making

Data forms the very basis for the decision making of a business. Big data testing helps in providing businesses with the right and helpful data. It enables businesses to find out the potential risks as well as opportunities and paves the way for better growth and success. With the appropriate data, free of errors, the businesses can make the right decisions.

3. Increased Business Profits

With the precise analysis of data, big data automation testing provides the business with high-quality data. It enables businesses to optimize their business strategies in order to get the best outcome. With the well-structured data, businesses are sure to increase their profits while lowering costs.

Also Read: Big Data and Analytics Testing: Learning the Basics

Stages of Big Data Testing

The big data testing is classified into three major stages. The three stages are:

1. Data Staging Validation

The first stage includes process validation, which is referred to as the Pre-Hadoop stage. In this step, the data that has been collected from different sources like weblogs, RDBMS, and more are verified and added to the system. In order to ensure that all the data matches properly, it is important to compare the source data with the Hadoop system data.

2. Process Validation

In this stage, the tester has to validate the accuracy of the data that has been obtained through big data application after the processing. It also includes testing the accuracy of data generated after the process of Map Reduce.

3. Output Validation

Output validation is the third and final stage of the big data testing process. It this stage, the tester ensures and validates the appropriate storage of the big data application output in the data warehouse. The final stage also includes the verification of the accuracy of data that is represented in the target UI like the business intelligence system. It also includes checking the data integrity in the target system and finding out any data corruption.

Also Read: Top 5 Big Data Testing Challenges

The Major Types of Testing in Big Data Automation Testing

There are various types of testing involved in big data automation testing. Some of the important testing types include:

1. Performance Testing

Performance testing service focuses on areas like data loading and throughput, sub-system performance, and data processing speed. In the data loading and throughput test, performance testing determines the rate at which the system can avail the data from different sources. It also checks the time taken for processing messages and creating data in the data store. In the data processing test, performance testing finds out the speed of data processing of the map-reduce jobs. In the sub-system performance test, the performance of the different individual components is tested.

Also Read: Key Performance Metrics for Effective Performance Testing

2. Architecture Testing

Architecture testing is important in order to prevent issues like node failure, performance degradation, high data latency, and more. Architecture testing helps in the establishment of appropriate Hadoop architecture. With a well-structured architecture of big data applications, businesses can achieve better and smoother operations.

Being a complicated process, big data automation testing requires to be performed by the highly skilled testers. With the use of the right testing tools for the different processes, optimum results can be achieved. In order to enhance the performance of a business and to stay ahead of competitors, big data testing has become the need of the hour. With big data testing, achieving greater success and increasing business profits become simple and easy.