Journey From Big Data to Smart Data: How Big Data Testing Can Help You?

The idea of big data can be traced decades back to the 20th century. However, it was only after the 21st century’s information explosion on the internet that businesses started discussing the real-life application of big data.?

Companies today are privy to large volumes of data, called big data. So, businesses need to keep this big data at their disposal.??

Nonetheless, the real deal with big data is its analysis. This is also where the journey from big data to smart data begins.?

The analysis of big data involves uncovering hidden patterns, establishing a correlation between different data sets, and gathering relevant insights. This form of analysis is also quite distinct and advanced from the usual data processing that most companies execute on a regular basis.?

Due to such high levels of sophistication, businesses encounter several challenges while transforming big data to smart data. If these challenges are not tackled early in the big data application development life cycle, they possess even a bigger threat in the data assurance process as well.?

Most of these challenges are common across industry. Some of the crucial ones are mentioned below:?

Understanding the data?

Big data is not a monolith; it is characterized by four V's – volume, variety, velocity and veracity. Enterprises can further sub-characterize big data depending on their business requirement. Based on these characterizations, businesses can actually move ahead with the data analysis process.?

In the digital world, everything comes in the form of data. For businesses, one of the significant challenges here is to deal with those scenarios when emotions and sentiments are presented as data. So, it's up to the business to decide when to perform a qualitative analysis and when to evaluate the data quantitatively.?

Real-time scalability & performance?

Data can be in any of the three formats – structured, semi-structured, and unstructured. Businesses can efficiently process structured data. But, when it comes to semi-structured and unstructured data, enterprises face challenges related to scalability and performance.?

To remain ahead of the curve in this competitive domain of big data, businesses need to perform functions on a real-time basis. So, working on such large volumes of differentiated data sets and getting real-time results is the most significant part of the challenge.?

Data integrity & security?

Dealing with such a massive amount of complex data sets also opens Pandora's box of cybersecurity implications associated with it. Most of this data also includes confidential and financial information of people. So, processing these data sets on multiple nodes involves a high degree of security risk for businesses.?

Moreover, some security threats are so stealth that even the popular database software systems cannot detect them. In addition, most software systems are open source, making them prone to cybersecurity threats.?

How big data testing helps businesses overcome these challenges??

Traditional software testing frameworks are not fully equipped to handle the challenges pertaining to big data. However, big data testing uses unique evaluation strategies to suit the various characterizations and complexities of big data.??

It starts with proper data validation.?

The validation stage is classified into three key major components:?

• Data validation: It involves verifying the apparently accurate and uncorrupted data from various sources such as scanners, logs, sensors, etc.?
• Process validation: Here, the data is checked for its accuracy. QA testers validate the business logic for various nodes at every node point while also verifying the key-value pair generation.?
• Outcome validation: Here, software testers verify the data stored in the EDW (Enterprise Data Warehouse) for any corruption or distortion.?

A large chunk of the testing efforts goes into data validation due to such a thorough validation process. This elaborative data validation testing process also helps QA testers overcome the challenge of discerning complex data sets.?

Next comes parallelism and performance testing?

While parallelism takes care of the scalability issues, performance testing can validate the actual performance of big data applications.?

It is recommended that QA testers conduct parallelism right at the CPU level through data partitioning. This would ensure that different processes are not independent of each other are executed simultaneously.? Moreover, projects must also be segregated into independent and inter-dependent projects for the seamless functioning of simultaneous operations.?

As parallelism helps QA testers in scalability issues, big data performance testing helps them tackle issues pertaining to memory utilization, CPU optimization, throughput, etc.??

Big data performance testing is quite different from the usual performance testing. Right from setting up a customized test environment to creating tailored test scripts, there are numerous stages of complexities involved in performance testing big data applications.??

Therefore, businesses need to make sure if they can undertake it themselves or whether they need to outsource it to experienced and professional quality engineering experts who are proficient in quality and technology assurance.?

Security testing big data applications?

Penetration testing or security testing helps businesses in gauging how well-protected is their data security infrastructure. However, security testing of big data applications can get quite complicated because of multilevel authentications and encryptions. Therefore, QA testers working on security testing need to be experienced and adept with the process.?

To ensure a seamless penetration testing process, QA testers validate the configuration efficiency of role-based access control (RBAC) rules while also checking the big data software application’s architectural integrity. Moreover, checking the network's security scanning efficiency also comes under the pen testing process, thereby ensuring a robust security architecture.?

Final thoughts?

Businesses who want to complete this journey of big data to smart data successfully cannot just rely on a set of data experts or professional testers. Instead, they need cross-functional teams comprising data scientists, quality engineers, ethical hackers, etc., along with a bunch of advanced testing tools.??

Having all these resources in one place can be a costly affair. There are a few organizations that can undertake this pursuit, but not all of them. So, in all those cases, businesses seek help and guidance from quality engineering and technology assurance firms.?