Organizations today are constantly looking for different ways to leverage the dominance of big data to gain a competitive edge over their competitors. A massive set of raw data needs a careful design in testing to ensure the desired outcomes are achieved when the application or particular service goes live. Also, not forgetting that testing becomes crucial with the exponential growth in the number of big data applications globally.
Although Google hasn't shared the exact number, however, according to skai.io, Google handles around 3.8 million searches per minute globally.
Today, Internet has become so powerful and dominant because of data. Whenever people search for something, it's always about searching for data about data. And this is possible and fruitful only because someone has preserved this information when you search for it somewhere over the Internet. Again, it's all due to the technology that is evolving. With the advancement of technology, we also witness a surge in demand for substantial data applications, which obviously need extensive focus on software testing services.
Decades back, data was preserved by using simple data files. However, when the complexity of data grew, database management came into existence. A few years later, both structured and unstructured data were generated at a giant level, thereby gaining a term called "big data."
In this blog, we will learn what big data testing is, its benefits, and the effective strategies to implement it.
What Is Big Data Testing?
In simple words, big data testing is a software testing procedure that involves examining and validating the functionality and performance of big data applications. When we talk about big data, it implies a huge amount of data that traditional storage systems cannot handle.
This is why such type of testing can require some special tools and techniques that we will discuss later in this blog.
Benefits Of Big Data Testing
A company should leverage big data testing to locate qualitative intact, full, accurate, and efficient data. It is essential to know and confirm that the data collected from different sources and channels functions as expected or required. Testing big data can leverage several benefits. Let's discuss some of them:
It is a requirement of utmost importance for business planning, decision making, and forecasting. For any big data application, it is vital to ensure that the data used is accurate. And therefore, testing helps us to know whether the data injection process is error-free or not. It also helps ensure that the data process validation works appropriately, mainly based on the design logic.
Overall enhancement of business strategy
Accurate data is the heart of any critical business. The dissemination of suitable data applications or services can help analyze all kinds of risks it poses to the companies. This way, the data testing will improve the decision-making process and help in effective decision-making.
Choosing the right data at the right time
A big data framework would consist of multiple components. Hence, any member can also lead to bad performance or hindrance in data loading or processing. The accuracy of the data will only be fruitful if it is available at the right time. In this scenario, applications can undergo low testing with different volumes and varieties of data. In this way, they can quickly process a large amount of data and disseminate the available information when required.
Poor data can become a major loophole for the business as it can be difficult to determine the cause and location of errors. On the other hand, if the companies would leverage big data testing, it can guarantee accurate data, which would improve the overall business and help isolate the important from the unstructured or bad data. In this case, the chances of errors will be diminished, ultimately enhancing customer services and boosting revenue.
Read About How To Implement Big Data Testing In Healthcare
How To Implement Big Data Testing In Healthcare?
Practices For Handling Big Data Testing
Performing comprehensive testing of Big Data requires great expert knowledge to achieve robust results within the defined budget and timeline. This can be done by adhering to some effective practices for testing big data applications. Let's explore them quickly:
The testers must work on the right datasets based on the requirements and the test cases, catering to the complexity of the data. When we get to know about the required format, it will help us identify the test management tool that can help deal with requirements for testing an application. In this case, it is important to choose a test data management solution that ensures effective coverage analysis and enhances the data visualization.
The current landscape wherein organizations are implementing a child methodology, and it is possible that data can be sourced even from the actual users. Therefore, the test data must be protected against any breach in the development process. The biggest security of the data is also of utmost importance. Sensitive personal data such as name, address, contact information, and financial information should not get leaked. Therefore, it is critical that the test data is validated and the resulting test cases give a real-time picture of the production environment whenever the application goes live.
Choosing automation for the whole process
Testing big data includes scripting, data masking, cloning, and provisioning. Leveraging automation testing for all of these is effective as it will accelerate the process and make it much more efficient. Automation tools can help ensure that the data is provided in the expected format whenever required. Even the production of test data can be automated. It will eventually help save time in the longer run, reduce efforts, and expose any sort of error with the data on an ongoing basis. Other than this, the queue a team can also help in streamlining and validating the data management efforts.
Focus on the testing environment
A test environment is of vital importance as it provides accurate feedback about the quality and behavior of the application under test. It is important that the testing environment has enough space for the storage and processing of a large amount of data. It should also have minimum CPU and memory utilization to keep performance high to test big data performance.
Another strategy to manage big data testing is to ensure that the test objective is clear. Sometimes the communication gap between the testers and stakeholders can also cause a hindrance in the process of big data testing. This is why it is important to stay connected with the objective of big data testing.
What Should You Include In Big Data Testing?
There are some big data testing methods or types that one needs to know. Let's discuss some of them:
It is a front-end application testing that provides a boost to data validation. It compares the real results produced by the front-end application against the expected results.
The automation process in big data allows one to test performance in different scenarios, such as testing the application with different varieties in volumes of data.
It helps verify that all the data is extracted and loaded correctly within the big data application.
In this type of big data automation testing, the QA testers can verify the output data and know whether it is correctly loaded into the warehouse by comparing it with the warehouse data.
This type of big data testing validates that data migration from the old system to the new system witnesses minimal downtime with exactly no data loss.
Data is the heart of almost every business in today's time. It is crucial, and therefore it can't be overlooked as it helps in estimating the expected and some of the most unexpected unknown factors.
In this case, there is a need to manage big data, as it will overall boost the software quality assurance. It is also important to test the application's functionality, security, performance, and every aspect to ensure the desired customer experience and ultimately gain a huge revenue in business.
Here, at Bugraptors, we provide robust test data management practices for complex and huge enterprise platforms. We believe in delivering efficient and accurate results to provide companies with appropriate solutions.
For more information, contact us at firstname.lastname@example.org