What is Big Data Testing and How Has the COVID-19 Pandemic Impacted It?
What is big data testing?
Big data is a huge, and sometimes unlimited, set of data that cannot be processed via typical methods. Quite a lot of businesses, especially large ones, work with big data, and since the correctness and efficiency of commercial decisions are based largely on this data it must be accurate and reliable.
Data usually streams in, in real-time, so normal testing methods simply cannot process such a plethora of data.
In order to test bid data you need to use different types of testing, this includes functional and non-functional testing, to ensure error-free processing.
This includes the following types of tests:
- Data reception test. Data almost always comes from different sources, for example, CSV database files and information from social networks. So, the main task with this test is to check the correctness of the extracted data. There should be no inconsistency or corruption in this data set.
- Processing test. Here the data should be shown in a summary form. So, businesses can quickly come to conclusions based on these quick summaries.
- Performance testing of big data processing. The purpose of this test is not only to check the performance of the system, but also to identify bottlenecks. Doing this means that something can be done to improve the system. Analysis of parameters such as capacity, processing time, memory used will be done during this test.
- Fault tolerance test. During this type of testing, experts will check to see why and when part of the system failed. They are able to determine whether or not the system is capable of working under such conditions again, and how effective the system is. Plus, this test checks the operation at maximum loads, so you can see how stable the system is under extreme pressure.
The types of tests listed above are basic, but additional tests can be carried out to support the above tests, if need be.
Why do we need big data testing?
The main task of big data testing is to give businesses objective data, so that they can proceed to make business decisions.
We are now witnessing an unprecedented increase in the amount of information that has to be processed. In addition to the fact that the volume of data is constantly growing, it’s also coming through from several different forms, including; smartphones, tablets, social networks, information from databases, information from digital devices (IoT).
All of this data helps businesses to respond faster to market changes, and to offer their customers exactly what they need right now.
The only effective way to get a company to handle vast quantities of data is to implement a big data testing system.
The COVID-19 pandemic played its role in this matter, because clients and business had to rapidly change tactics to survive in the ever-changing environment. It was critical to work fast, to gather data, analyze data and to act on this data in a timely manner.
Today large and medium-sized businesses must implement big data testing systems into their working systems. But soon small companies will also need to use big data testing.
Benefits and drawbacks of big data testing
The main advantage of implementing a big data testing system is that you can check the quality and integrity of large data sets as they stream in. Thus, the data is protected from degradation and redundancy. Businesses thrive when the data they act on is correct and reliable.
Another benefit of big data testing is, you can quickly scale a dataset. Many applications do an excellent job with small amounts of data, but as the processing scale increases, their efficiency decreases noticeably, especially if this data comes with delays or problems. Therefore, ensuring high efficiency and smooth processing is the undoubted advantage of big data testing.
Another benefit is reduced downtime due to big data processing problems. This is especially true for large businesses, because the cost of their downtime, even for 1 day, can cost millions of dollars. An effective big data testing system can ensure the reliability and continuity of the data flow, and it works with all structural units, thus eliminating downtime and all the losses associated with it.
Other benefits of big data testing are:
- Improving management efficiency. Management can make better decisions because the data is of sound quality and is reliable.
- Advantage over competitors. If a company has a functioning big data testing system, and its competitors do not, the company has a clear advantage.
- Rapid response to changes. As we said, the existing big data testing system will help eliminate downtime and accelerate response rates to changes in the market. Which can significantly improve the overall performance of the business.
- Income growth. No downtime, fast responses to changes, quality and reliability of data = business will see revenue growth.
And now, a few words about the disadvantages of big data testing. They are largely associated with insufficient knowledge and experience in the implementation of these technologies.
Poor knowledge of the tech involved and a lack of qualified specialists – this is perhaps the main problem of implementing big data testing solutions.
The need for significant initial investment. Significant investments are required at the implementation stage of a big data testing system, although they will quickly pay off with increased revenues and reduced costs for other things.
Scaling problems. As practice shows, many systems for processing data, even though they are designed to process large data volumes, do not always respond well under heavy workload, this causes potential problems and requires additional attention.
The following things can greatly reduce the performance of big data testing systems.
- Greater data fragmentation. The more data sources and types of data there are, the more difficult it is to cope with the flow.
- Setting up a test environment. Creating an efficient test environment (people and machines) can be very challenging, especially for very large businesses.
- Lack of required competencies. If the data testing team does not understand the nature of the data, it will be difficult to manage the data that comes in.
But all of the disadvantages above can be rectified in time. Plus, the potential benefits of implementing an effective testing system outweigh any potential costs.
How has the pandemic affected big data testing?
Undoubtedly, the COVID-19 pandemic has impacted all areas of the economy, and not to mention the everyday life of a person. However, the pandemic has actually positively impacted the bid data testing field.
- Restrictions on leaving home, especially travelling to other cities, provoked a surge in online commerce. Naturally, this led to an increase in the amount of data that needed to be processed.
- Growth of social media networks due to the restrictions, also lead to an increase in the volume of data being processed.
- Increased traffic on the internet - for example more people wanted to know how they could conquer the pandemic. High volume of searches for topics like vaccination centers, information on restrictive measures, number of reported cases, increased the amount of data that needed to be processed.
Today big data testing plays a critical role in all of the above cases. It can diversify production, accelerate vaccine development, and accumulate up-to-date knowledge.
Big data is an asset that can help you to quickly assess, predict and respond to the spread of the disease, in order to reduce its negative impact. Effective big data testing is the foundation of this endeavor.
Big data is not just growing, it’s growing extremely quickly. Which means that there is a demand for new testing solutions, as well as for specialists in this field. And as the recent pandemic’s events have shown us, the quality and speed of big data processing can make a huge difference.
Consequently, companies should now pay attention to establishing effective big data testing techniques and they need to find the right specialists for the job, or train their staff to work with such systems.