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HiTech
6 minutes read

Big Data Testing

By Jonathan Tarud
By Jonathan Tarud
HiTech
6 minutes read

Big Data testing has proven to be an essential part of modern IT as organizations rely more heavily on collecting and analyzing data. As a result, Big Data applications were developed to help organizations manage, store, and analyze the vast amounts of data they collected from their web services and mobile apps

However, with so much data being collected, Big Data solutions need to be functioning correctly for businesses to benefit from their capabilities. This post will explain what Big Data testing is, the common types of testing done on Big Data, and some of the challenges associated with Big Data testing. 

What Is Big Data Testing? 

Big Data testing is the process of verifying that all facets and functions of a Big Data application work as expected. The primary purpose of testing Big Data apps is to ensure that they run smoothly with no errors while maintaining high-performance levels and security standards. 

This task might sound straightforward, but it is not. Big Data involves a collection of diverse datasets that are too large for traditional computing techniques to process. As a result, it takes a skilled professional and several different tools and techniques to properly test a Big Data app. 

Every aspect of Big Data from data creation, storage, retrieval, and analysis must be tested to ensure proper functionality and performance levels. Plus, there are non-functional tests covering additional aspects of Big Data such as security, data quality, and infrastructure. 

As you can see, Big Data testing involves many different tasks. And, if you want your organization’s investment into Big Data to be worthwhile, you will need to thoroughly test your solution to ensure that this HiTech data processing application meets your expectations and fulfills your goals. 

Common Types of Big Data Testing 

As we have already briefly mentioned, several different facets of Big Data need to be tested to ensure full functionality and performance. Broadly, Big Data testing can be broken down into the following tests:

  • Functional testing 
  • Performance testing 
  • Non-functional testing 
  • Security testing
  • Integration testing 

Within these broader testing categories, there may be more specific tasks and tests to perform. For example, technically, security testing is part of non-functional testing. However, we believe security is important enough to warrant its own sub-section. Therefore, we will discuss the critical aspects of each testing category so you understand what goes into each task. 

Functional Testing 

Big Data applications possess several operational and analytical parts that need to be thoroughly tested. In addition, Big Data apps are built to handle vast amounts of data. As a result, there are typically several components and APIs being used to power the Big Data solution’s functionality. Functional testing tests every feature and API being used in a Big Data application. 

Ideally, each API and component should be tested in isolation before its functionality is tested in the broader context of the Big Data solution. Only after the function of each element and API is validated should end-to-end testing be conducted. If executed properly, these tests will ensure the seamless performance of your Big Data application. 

Performance Testing 

Performance testing is concerned with the performance of each component and API and the Big Data system as a whole. In addition, testers will be concerned with the app’s actual access latency. For example, the application’s response time could vary based on the user’s geographic location based on the network throughput in their region. 

Big Data testers will also be concerned with determining the application’s processing capacity. Additionally, the application’s handling of stress loads and optimal resource consumption will be tested and noted during performance testing. Finally, performance testers should also determine the scalability options for the Big Data application during this stage of testing. 

Non-Functional Testing 

While non-functional testing doesn’t pertain to the technical performance of the application, without non-functional elements working at optimal levels, Big Data applications offer little value to organizations. There are three crucial non-functional facets to consider: data quality, infrastructure, and security. We will cover security in a separate sub-section. 

Data quality testing ensures that there are no erroneous data records or messages. The key facets of data quality that testers need to consider are:

  • Accuracy 
  • Precision 
  • Timeliness 
  • Consistency

Without quality data, the insights provided by your Big Data solution will be incomplete or off-base. Therefore, it is vital to ensure that the data being collected, stored, and analyzed is high quality. 

Additionally, testers need to ensure that a Big Data application’s infrastructure provides continuous service availability in internal and external data systems. A solid infrastructure will also minimize or eliminate data replication, backup, and restoration issues. 

Security Testing

Data security is something that all businesses need to take seriously. Arguably, data security is an essential aspect of a Big Data solution. Security testers will ensure that sensitive data is protected at all times. They accomplish this task by validating data encryption standards for data at rest and in transit. 

Another area of interest during the security testing stage of Big Data testing will be the application’s architecture. Flaws in the architecture can lead to exploitable weaknesses. Testers will also validate user authentication measures and role-based access authorization levels. Data security specialists should also conduct additional penetration, application, and network testing. 

Integration Testing 

Typical Big Data applications rely on several components and APIs to function correctly. While the functionality of all of these components is tested thoroughly during functional testing, integration testing validates that all of the APIs, features, and third-party software solutions are correctly integrated. Integration testers also review the application’s architecture and tech stack to validate compatibility.

Seamless communication between third-party software and Big Data applications is critical to the success of Big Data solutions. 

The Challenges of Testing Big Data

Testing Big Data is critical to the ultimate success and viability of the insights your organization is able to gain from your solutions. However, Big Data testing is not as simple as it sounds on the surface. The first significant challenge is finding the correct tester. Testing Big Data is complicated, and it requires a highly skilled professional. 

The next major challenge of Big Data testing is that there are no tools that can be used for end-to-end testing. As a result, testers must use a combination of different frameworks and testing tools to properly test their data solution. Compounding this issue is the sheer volume of the data that needs to be tested.

Finally, testing Big Data requires a high degree of scripting to design test cases. Writing these scripts can take time, and they need a skilled tester to create tests that will adequately test the Big Data solution. 

Final Thoughts 

Big Data provides a lot of value for organizations that want to get more from their data. However, without adequate testing, businesses can’t be sure that their Big Data applications are working as intended. If you want to learn more about Big Data testing, reach out to an experienced app development partner.

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