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5 Benefits of Big Data Testing

5 Benefits of Big Data Testing

The internet today is like Pandora's Box. The question here is, "How did the Internet become so powerful?" The answer is with data, and data about data. When you search the internet for something, you are searching data about data. Those searches provide us with useful information only because someone has preserved this information somewhere over the internet, and as this technology advanced, the demand for Big data applications was created.

In earlier times, data was not complex as it is today and the data was preserved using simple data files. As the data grew to be more complex, database management systems came into existence. However, as technology advanced, both structured and unstructured data were being generated at a giant level, gaining the title "BIG DATA". This generated the demand for Big data applications. Below is a study on data growth from around the world:


Source: IDC’s Digital Universe Study

Big data is not limited to storing data – it aims at processing and generating useful information. Nowadays, a lot of structured and unstructured data is created. This data is useful only when it is precisely tagged and analyzed. To ensure that the data is processed without any error, it is important that Big data applications are thoroughly tested.

Primary Forms of Testing for Big data applications:

Functional Testing: The front-end, self-explanatory term, is part of an application which is made available to the actual users of that application. But, the Big data application needs to be tested for both front-end and back-end. Front-end testing of a Big data application is not different than testing any other software system, but the back-end testing may require basic knowledge about the framework and the various components involved.

Performance Testing: This testing is performed under different conditions, such as testing the application with different varieties and volumes of data. Testing Big data applications for performance is different than performance testing of traditional applications. There is a growing need for performance testing of Big data applications in order to ensure that the components involved provide efficient storage, processing, and retrieval capabilities for large data sets.

Big data testing is a gradual process which is comprised of consecutive steps, these steps can be generalized into 3 major categories:

Data Validation: Big data sources are heterogeneous - data can enter the system from multiple endpoints like server logs, surveys and feedback. It's vital that the data that makes it way to the Big data framework is validated beforehand. During data validation, correct data is extracted and is compared with the source data so that they match and can be loaded into the system.

Data Process Validation: Once the data validation is confirmed and data enters the system, the verification of business logic takes place. This involves validating whether the processed data is accurate or not.

Output Validation: This is an inevitable part of Big Data testing, as during this stage data files are generated and validated for data integrity and corrupted data is annulled. Once the files are successfully validated, they are ready to be shifted to further systems such as a data warehouse.

Benefits Of Big Data Testing:

Big data testing helps you find the qualitative, accurate and intact data. The data collected from different sources and channels is confirmed, which enables application improvement. Below are some of the primary benefits of Big data testing:

1. Data Accuracy: Every organization strives for accurate data which may be used for business planning, forecasting and decision-making. The data needs to be validated for its correctness in any Big data application. This can be done by ensuring that:
  • The data injection process is error-free
  • Complete and correct data is loaded to the Big data framework
  • The data process validation is working properly based on the designed logic
  • The data output in the data access tools is accurate as per the requirement
2. Cost-effective Storage: Behind every Big data application, there are multiple machines that are used to store the data injected from different servers into the Big data framework. Every data requires storage, and storage doesn't come cheap. So, it’s important to thoroughly validate if the injected data is properly stored in different nodes based on the configuration such as data replication factor and data block size. Also, if the data is not well structured, it definitely will be in bad shape and will require more storage. And once that data is tested and gets structured, the less storage it will consume, and this ultimately becomes cost-effective.

3. Effective Decision-making And Business Strategy: Accurate data is the pillar for crucial business decisions. When the right data goes in the hands of genuine people, it becomes a positive feature. It helps in analyzing all kinds of risks and only the data that contribute to the decision-making process comes into the picture, and ultimately becomes a great aid to make sound decisions.

4. Right Data At The Right Time: Big data framework consists of multiple components. Any component can lead to bad performance in data loading or processing. No matter how accurate the data may be, it is of no use if it is not available at the right time. Applications that undergo load testing with different volumes and varieties of data can quickly process a large amount of data and makes the information available when required.

5. Reduces Deficit and Boosts Profits: Indigent Big data becomes a major loophole for the business as it is difficult to determine the cause and location of errors. On the other hand, accurate data improves the overall business, including the decision-making process. Testing such data isolates the useful data from the unstructured or bad data, which will enhance customer services and boost business revenue.

Performing comprehensive testing of Big data requires expert knowledge in order to achieve robust results within the defined timeline and budget. Making use of a dedicated team of QA experts with extensive experience in testing Big data will allow you to access the best practices for testing Big data applications. For more information, get in touch with our team today.

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This publication is for informational purposes only and nothing contained in it should be considered legal advice. We expressly disclaim any warranty or responsibility for damages arising out of this information and encourage you to consult with legal counsel regarding your specific needs. We do not undertake any duty to update previously posted materials.