Since the mid-’90s, there has been a phenomenal explosion in the amount of information technology tools being utilized by most businesses across the globe. The velocity, as well as the volume of data that is consumed by business, has outpaced the projections and it has led to an inherent need for big data testing in real-time.
Today, it has become more and more cumbersome to ensure the quality of the big data ecosystems deployed in both big and small businesses. The engineers in the field known as “Testers”, are presently processing and cleaning structured data however, there is also a need to consume semi-structured and unstructured data due to the sheer volume of data being generated every second.
The major challenges that we face currently in big data testing can be categorized into three distinct points which are as follows:
- Security of the data
- Scalability in the media that stores all your data
- System load and performance owing to accelerated volumes
Taking care of these challenges will be tantamount to verify the entire continuum of big data testing. The three foundational pillars of big data testing require expert maintenance in order to mitigate the risks and take on the challenges that are ahead of us. These three pillars can be summarized into the following aspects:
- Data warehouse testing
- Performance testing
- Test data management
Streamline Your Processes to Control Your Challenges
The ever-evolving landscape of technology renders the necessities of today completely obsolete tomorrow. Hence it is imperative that businesses push the data scientists and engineers to create processes that stand the test of time even if the technology or the platforms have evolved in the future. Big data testing needs to follow the same evolutionary cycle as that of software testing. To handle the dynamic nature of the advances in technology, organizations must think about how they manage the aforementioned three pillars. Let us look at them one by one to understand how each of the pillars helps your company remain ahead of the challenges –
Data Warehousing and Testing
Although testing is performed in a strictly controlled and isolated environment, the inherent nature of this test environment remains unpredictable hence it posits a unique set of challenges. It requires highly complex processes, tools and testing strategies that pertain to the volume, variety, velocity and veracity (4Vs) of big data. Some recommendations for mitigations include:
- Normalization of tests and designs – By normalizing the test schemes at the design level you can generate a better set of normalized data for the big data testing.
- Divide and conquer the testing requirements – Making the big elements simple by dividing them into smaller fragments will enable efficient use of resources and reduce testing times drastically. The big data warehouse must be organized into small units that can be easily and quickly tested and achieve higher optimizations and test coverage.
- Measure your 4Vs regularly – Test environments for data warehouses that are designed to keep the 4Vs at the centre and that can take care of the 4Vs of your big data will give you improved coverage of the tests being carried out.
Testing the Performance to Build Strength
Any system that involves huge volumes of data will eventually break down due to fatigue. Hence, testing for performance for issues from time to time will enable the organizations to keep ahead of the load on their systems. Performance testing concerns the real-time scenarios, data volumes, navigation habits of the end-users and workloads. The performance of your big data is a total of multiple factors including hardware, network, database servers, web servers, number of peak loads, hosting servers, etc. Maintaining the system that tests the performance of your big data requires full attention from the organization. Some of the recommendations for performance testing of the test systems are:
- Simulating a real-time environment – Both parallel and distributed workloads is essential for testing. Parallel testing in a distributed environment will generate the scripts through your performance testing tools which can later be distributed among different controllers that simulate the real-time environment.
- Execution of parallel tests – Test execution handling is effective when a host of distributed virtual users are empowered to execute tests in a parallel fashion.
- Work with distributed test data – The scenario set of the controllers predominantly influence the strategies utilized for performance testing. The back-end databases and the spreadsheets where the test data resides seldom have the ability to process or store unstructured big data. In order to work around this hurdle, an interface needs to be developed which can enable the controller to interact with the existing test data which is distributed.
Quality of the Test Data is Crucial
Managing the test data itself is one of the major challenges which keeps the IT infrastructure of the organizations on its toes. Some recommendations to address the pain points of this third and last pillar are as follows:
- Design and plan well – In order to test big data, automated scripts can be stretched only so far. If you do not want extended response times and possible time-outs during test executions, then adequately designing and planning for your test data sets is a crucial element that cannot be overlooked. Action-based testing or ABT helps organizations mitigate this. The individual tests in ABT are treated like they are actions in the test module. These actions are then directed towards keywords in conjunction with parameters required that are used to execute tests.
- Setting up the right infrastructure – It all begins with what kind of resources and capacity you have to test your big data. It is a no-brainer that the better the tools, hardware and their capacities, the better testing capabilities an organization will have. Having said that, better does not always translate to expensive or top of the line. Enormous resources are consumed by test automation to generate the desired workloads. The future is cloud and organizations must look towards harnessing the power of the cloud to strike a balance between the overall costs and the quality. Virtual parallelism on a number of virtual machines also provides an alternative for generating higher workloads for the performance testing of big data systems.
Not a Distant Dream but a Sound Reality
Big data testing is in its infancy currently. There are limited manuals and frameworks that help organizations and IT teams automate the testing scenarios. Significant upgrades will be needed in the future in the quality assurance processes, customized frameworks used and tools utilized in different specialized testing services. As Big Data gets bigger with every passing day, these challenges are only the tip of the iceberg that is visible today.
Unprecedented issues and risks are looming around the corner waiting to emerge as the testing community tackles the goliath that is Big Data. What was garbage data in the yesteryears, is now big data because today nothing is being deleted, removed or wasted. All data sets whether structured or otherwise are of equal importance for businesses that want to make informed decisions in a timely manner to drive their profits and increase their footprint in the market.