5 things you need to know about Big Data testing
The challenges of Big Data testing triggered by the 3Vs are topped with rising costs and the demand for excellent technical expertise.
We live in the world that is approaching 3 zettabytes (ZB) of data. To put that in perspective, if a GB was a brick, with a ZB, we could build over 250 Great Walls of China. This is due to a high rate of user-generated content, like that 100TB (1 TB= 1 large PC hard drive) of data uploaded to Facebook daily.
Most of the times, Big Data is the answer to complex and diverse business problems and can offer solutions on the spot. The 3Vs (volume, velocity, and variety) that define it make testing require specialized tools and experienced personnel. For a company aiming to harness the power provided by Big Data, it is necessary to get ready for some of the challenges related to testing and ensuring the accuracy of the sources they use. Compatibility and security should also be on the short list of priorities.
How is Big Data testing different?
The world of software testing revolved around making sure that programs behaved as expected and parts fit together flawlessly, as well as that there were no connectivity issues and security risen to standards. Testing data either came from the client or dummy data used for calibration, yet the focus was on scenarios and behavior of functions and procedures. Although not indicated, all tests could have been performed manually.
This paradigm is reversed completely in case of Big Data testing, where everything revolves around making sure the data is correct, and that it moves according to plan from the source towards the output through the map-reduce, a transformation that aggregates data following the company’s business rules’ logic. Therefore, Big Data testing is linked to the ETL (extract, transform, load) process; a world away from traditional testing.
Challenges of Big Data Testing
The challenges of Big Data are not dictated by the volume, but more by the high-velocity and high-variety. Managing and ensuring the quality of a diverse and fast-growing entity requires different tools and is not achievable by just scaling existing capabilities.
Automation is mandatory
Since the sheer volume of data calls for extended processing power and takes longer-than-regular software testing, performing manual testing is no longer a viable option. Yet, automation requires significantly more knowledge. Creating automatic scripts that capture flaws can only be done by programmers, which means that middle-level, manual testers, and black-box testers can’t find their place in this environment without upgrading their skills.
Higher technical expertise
The technical knowledge necessary to handle Big Data doesn’t include only testers but developers and project managers. The experts working with these systems need to be proficient in Hadoop, which is the primary Big Data framework, and adjacent technologies such as Pig, Hive, Java, JUnit and more. Prior knowledge of relational databases, SQL, can help but need to be completed with NoSQL necessary to access unstructured data. To learn Hadoop, a background in Linux is preferred, and most companies ask for 2-5 years of experience, as a representative from software testing company A1QA explains.
Complexity & integration problems
Since Big Data comes from a variety of sources, and formats are not always coordinated and compatible, it is necessary to check for integration with enterprise applications. For a solution to be functional, the input and output data flows should run freely, and the information is expected to be available in real time. A possible settlement to this is data virtualization, but that too needs to be thoroughly tested before becoming usable.
Cost challenges
As previously described, Big Data specialists don’t come cheap. You can subscribe for a pay-as-you-use solution, but that is mostly if your company’s needs can be satisfied by an off-the-shelf product. A customized package that requires development, integration, and testing represents a consistent investment. To save some costs, be sure to ask for a firm timeframe. Don’t forget to inquire about the testing method and accept as much automation as possible, or you will be looking at weeks of manual testing.
Logistical hazard
Currently, there is no end-to-end testing solution, and each part of the process requires particular attention. For a practical implementation, the company designing your Big Data-powered algorithms or dashboards needs access to real data from your organization to calibrate components as accurately as possible. However, this could come in contradiction to some of your internal security regulations regarding sharing sensitive data with third parties. Either get the necessary approvals or create dummy data as realistic as possible, in large quantities.
Collaborate with the testing team
Until now, a tester did not need to know many details about the final scope of the project and the underlying architecture. They just focused on one individual component at a time. All that has changed with Big Data. Now the success of a project involves a good collaboration between the company which provides the solution and the client. The tester needs to follow the entire logic to help avoid bottlenecks and ensure proper functioning of the components in an integrated environment. Also, on-site testing can contribute to reducing operational errors.
Data in the era of self-service
When choosing a Big Data package, pay close attention to the testing procedures and be sure to check that they have an answer for each of the challenges highlighted in this article. Furthermore, it is important to verify that the architecture of the proposed solution is ready to accept data prepared at the source (your company) so that you don’t spend additional money on data prep services, unless the application strictly requires this or you don’t have the necessary human resource.
Before engaging in a full-scale Big Data project, it is best to start small, just to get the employees used to the new way of working, focused on numbers and paying attention to every bit of information that is generated and stored. To prepare for success, get a solution that is suitable to the skill level of most users in your company, aiming at self-sufficiency.