Big Data Testing and its significance in the years to come

2016 was the year of Big Data; those leveraging big data are sure to rush ahead while those who do not must have fallen behind. Data evolving from mobile, purchase histories, social networks, CRM records, etc. provide companies with valuable insights to expose hidden patterns that can help organizations graph their growth story. Evidently, when we talk about data, we are discussing huge volumes that amount to petabytes, exabytes, and at times zettabytes.

Together with this huge volume, this data which initiates from different sources also needs to be managed at a speed that will make it significant to the organizations. To make this organizational data useful, it has to be anticipated by the users via applications.  

As with every other application, testing forms an imperative part; it is the same with Big Data applications as well. Though, testing Big Data applications has more to do with validation of the data than testing the individual features. There are a few hitches that need to be crossed when it comes to testing a Big Data application.

As data information is fetched from various sources, it needs live integration, for it to be useful. This can be achieved by extensive testing of the data sources to make sure that the application does not have a scalability issue. Together with this, the application has to be tested thoroughly to enable live deployment.

The most imperative element for a tester, testing a big data application is the data itself. While testing Big Data applications, the tester wants to dig into semi-structured or unstructured data with changing schema. All these applications cannot be tested through ‘Sampling’ the same way in data warehouse applications. As Big Data applications comprise of very large data sets, testing has to be carried out with the help of appropriate research and development. So how should a tester go about testing the Big Data applications?

At the utmost level, the big data testing approach consists of both functional and non-functional components. Functional testing validates the quality of data as well as the processing of it. Test scenarios in data include correctness, completeness, lack of duplication, and much more. The processing of data can be done in three ways: real-time, interactive and batch; though they all comprise of movement of data. Thus, all big data testing strategies are based on the transform, extract, and load (ETL) process.

It originates by validating the data quality impending from the source databases, validating the transformation or process by which the data is structured. ETL testing comprises of three phases, i.e. Data staging, MapReduce validation, and the output validation phase from where the output files from the MapReduce are ready to be moved to the data warehouse.

ETL testing requires automation especially for the speed needed for big data, and there are tools available for each phase of the ETL process. The most renowned ones are Cassandra, Hadoop, Hive, and Mongo. Stay tuned for more details on Big Data testing.