Big Data Testing | Big Data Analytics

The never-ending demand for the creation, storage, retrieval, and analysis of massive amounts of data sparked the inspiration for the development of Big Data Testing. Testing such a massive amount of data necessitates the use of precise tools, outstanding structures, and clever tactics. In this blog, we discuss big data testing strategies.

Big Data Testing

The majority of users may wind up asking, “Why exactly do we need Big Data Testing?” You may have written the queries correctly, and your architecture may be perfectly fine. However, there could be numerous failure scenarios.

Big Data Testing is a method that involves assessing and validating the functionality of Big Data Applications. Big Data is a massive collection of data that typical storage techniques cannot handle.

Testing such a large volume of data would necessitate the use of specialized instruments and methodologies.

Big Data Testing Methodologies

Testing an application that manages terabytes of data would require a whole new level of ability and out-of-the-box thinking. The main and critical tests on which the Quality Assurance Team focuses are based on three scenarios:

• Batch Data Processing Test
• Real-Time Data Processing Test
• Interactive Data Processing Test

Batch Data Processing Test

The Batch Data Processing Test consists of test processes that execute data when the applications are in Batch Processing mode, where the applications are processed utilizing Batch Processing Storage units such as HDFS. Batch Process Testing mostly entails:

• running the application against erroneous inputs and altering the data volume

Real-Time Data Processing Test

When the program is in Real-Time Data Processing mode, the Real-Time Data Processing Test deals with the data. Real-Time Processing tools such as Spark are used to operate the program.

Real-Time testing entails testing the program in a real-time environment and checking its stability.

Interactive Data Processing Test

The Interactive Data Processing Test incorporates real-world test protocols that interact with the application from the perspective of a real-world user. HiveSQL and other interactive processing tools are used in the Interactive Data Processing mode.

Big Data Testing

The following stages comprise the general strategy for testing a Big Data Application.

• Data Ingestion
• Data Processing
• Validation of the Output

Data Ingestion

Using extraction tools, data is first loaded from the source into the Big Data System. HDFS, MongoDB, or another similar storage system might be used. The loaded data is then double-checked for mistakes and missing values.

Data Processing

The key-value pairs for the data are generated at this step. Later, the MapReduce logic is applied to all nodes, and the method is tested to see if it is working properly. A data validation process is carried out here to ensure that the output is as intended.

Validation of the Output

At this point, the created output is ready to be moved to the data warehouse. The transformation logic is validated here, as is the data integrity, and the key-value pairs at the location are validated for accuracy.

There are numerous categories in which to test a Big Data Application. A few of the more important categories are described here.

• Unit Testing
• Functional Testing
• Non-Functional Testing
• Performance Testing
• Architecture