The Tools and Challenges of Testing Big Data Analytics
Big Data analytics is a natural advancement of the business intelligence tools that have been around since Deming’s era. This change is dictated by the increasing volume and diversity of data available to companies due to the latest technological and communication innovations. Just the U.S. Internet users alone generate 2.6 million GB of data every minute by creating content, uploading images, tweeting and buying. Most of this data is irrelevant to a company looking to improve its bottom-line, but some of it can be gold. The real problem is separating the signal from the noise and correctly analyzing it.
Ensuring the quality of such data is a challenge in itself due to the 3Vs that best describe Big Data: volume, velocity, and variety. Neat tables with quarterly or monthly metrics are just a fraction of the data available to an organization these days. From information produced by sensors to CCTV footage, social media reviews or e-commerce websites, the unstructured or semi-structured data come with new testing challenges. Yet, it also opens up new perspectives when it comes to customization and improvement in industries like healthcare, retail, travel, finance, and public sector.
The Data Focus
Compared to regular software testing, it might come as a surprise that Big Data testing is more focused on the validation requirements. After data has been adequately checked for consistency, accuracy, and validity, the most critical aspect is performance testing, followed by functional testing.
The non-functional validation testing flow consists of three parts: data staging validation, process validation, and output validation.
The framework where Big Data gets handled will most likely be Hadoop or similar. The first defense line against mistakes and to sustain quality business recommendations is to make sure the raw data coming from various sources is correctly loaded in the system. This preliminary verification makes sure files are not corrupt, that the results are filed to the right partition, and synchronicity with the original source is in place. It might sound straightforward, but when you take into consideration that the data source could be a surveillance cam in a busy commercial center and the end result should be facial recognition of terrorists, you understand the stakes.
Due to its volume, Big Data can’t be processed on a single machine. Therefore, it needs to be distributed to several centers and the results aggregated. This process, called MapReduce, uses a central node and slave nodes. The business logic of the process needs to be tested on a single node and then on a bunch of nodes. The next testing step evaluates the communication between the slave nodes and the master. After the Reduce process is over, it is essential to check that the results were aggregated correctly and validate the consolidated outputs.
Also known as the ETL (extract, transform, load) testing, this step focuses on correctly moving the results from the Big Data processing point to the management systems and warehouses to be used for reports. At this stage, testing must ensure that the transformations were applied following the accepted procedures and the output is in the requested format. Testing also looks at the data to prevent corruption due to changes.
Apart from the accuracy of the information, a Big Data project requires excellent performance metrics, especially when dealing with streaming data. It is vital that the whole process flows smoothly and there are no bottlenecks and overloads. This is similar to checking that the diameter of the pipes can handle incoming water and nothing is clogging them.
As explained by the software testing company A1QA,this set of test covers behavior under specific loads, productivity under high volume, stability in stress conditions and the system’s ability to scale.
Performance is all about supervising metrics such as load time, processing speed, space requirements and more. Its role is to determine what is causing delays or slowdowns and how this can be improved. This bit can be automated as it will be probably run on different Big Data clusters or on the same set until satisfactory results are achieved. An unsatisfactory score in this part of testing should be improved through optimization of the processes.
As previously highlighted, Big Data testing completely differs from regular software QA. Not only are the classic frameworks unusable, but it requires a new mindset and a different approach.
The first challenge is that testers need to understand both the client’s requirements as well as the underlying architecture. Sometimes, a simple infrastructure change can affect performance and reliability. There is no room for underqualified QA personnel in Big Data, which makes it more expensive than regular testing.
Secondly, scalability is a principal obstacle. This problem comes up when applications are not designed to handle the volume of data provided by actual use. For example, if your company intends to implement social media sentiment tracking, you should be ready to handle incoming streams of data 24/7. A subsequent problem is related to integrating live or updated information into a system already running.
Lastly, it is worth looking at security issues, as breaches can affect millions and translate to reputation damage and legal problems. Testing should ensure protection mechanisms, such as encryption, at least for those features that have specific attributes such as personal and/or sensitive data.
Data analysis has always been at the core of the decision-making process in organizations, yet through Big Data it has come to a higher level of possibilities. It enables organizations to get significant cost reductions by identifying the most profitable patterns, it allows for better decision making by uncovering trends and can even suggest introducing new products or services. In this landscape, testing ensures that the quality of data going into the system is the highest, that the change doesn’t introduce fake information and that the system is running in line with the set parameters.