With the exponential growth in the number of big data applications in the world, Testing in big data applications is related to database, infrastructure and performance testing, and functional testing.
The advancement of technology is enabling the collection of a massive amount of data almost every second. And, big data has emerged as the buzzword that is used by huge companies that have an enormous amount of data to handle. Companies find it hard to manage the amount of information they collect. They also face significant challenges in end-to-end testing in optimum test environments. They require a robust data testing strategy.
Big data seems to be becoming an increasingly prevalent phenomenon and it is not without reason. As the world continues to generate a mind-boggling amount of data every single day, big data has become a crucial tool, allowing companies to gain critical insights into their businesses, customers, etc. However, in order to leverage the true potential presented by big data, this technology must be subjected to robust testing, i.e. gathered, analyzed, stored, and retrieved in accordance with a proper plan. Before we delve any deeper, let us walk through some of the key benefits of big data testing:
- Better data analysis: A robust big data testing strategy ensures the data involved is free from any issues, thus ensuring substantially improved quality of the insights gathered from the analysis of such data.
- Cost savings: Big data testing not only empowers companies with accurate insights but also with cost savings since the process of big data testing is not very expensive.
Time to see how big data testing works:
- Data ingestion testing: This type of testing starts off with data collated from a variety of sources, including social media, sensors, CSV, logs, etc., being stored in the Hadoop Distributed File System. Since the goal here is to make sure that data has been extracted and loaded into the HDFS accurately, the tester first has to see if the data ingestion has been done in accordance with the schema. Furthermore, the tester must also see to it that the data has not been corrupted.
- Data processing testing: With a focus on collected data, data processing testing involves verifying that the business logic has been implemented correctly.
- Data storage testing: This involves testing the output data stored in warehouses for ensuring that the data has been loaded correctly.
- Data migration testing: This is required only when the app needs a tech change or is being moved to a different server.
- Performance testing: Since big data apps must quickly process extensive amounts of data, it is imperative to execute performance testing to validate the architecture is sound and will be able to function as expected.
Now, let us see the primary components of big data testing:
- Data validation: The process starts with the Hadoop Distributed File System (HDFS) where data is organized and subjected to extensive checks to analyze its accuracy to ensure it is not corrupted.
- Process validation: Also called business logic validation, this stage involves the analysis of the business logic at each node point.
- Output validation: Output validation involves analysis to identify any distortions in the processed data.
Here is also a look at some of the most important big data testing tools:
- HBase
- Hadoop Distributed File System
- Hive
Finally, a look at the best practices:
- Ensure that the testing objectives are precise and unambiguous
- Automate the process to help save time
- Always make sure that testing is executed on the basis of the business’ individual requirements
There is no doubt that big data testing has and will continue to play a crucial part in helping companies glean the best possible value out of their data and drive their businesses’ growth. However, for that, you need to get your big data testing strategy correct. So, go ahead and start looking for a trusted service provider ASAP.
