Big data is now full-fledged as a modern-day technology which sets the base to many of the enterprise resource planning applications and platforms. In the current world of massive digitization, we are getting overloaded with various kinds of data, which needs to be captured, stored, and analyzed for various references. Mishandling of vital data may lead to many issues and disgruntled operations, hence the need for proper big data database testing.
Big Data acts as a solution to all such problems. It facilitates effective management of huge datasets which would other be difficult to handle with the traditional database management systems. Big data is all about storing, managing, retrieving, and analyzing data which is a bit in terms of volume. The approach is to use various kinds of big data tools to perform these operations at lowered cost and in a minimal timeframe.
After Big Data, there had been a radical change in the concept of database structuring and maintenance. Now, one may think of data warehousing in connection to big data. Similar to how we use its industrial terms, data warehousing is all about storing huge amount of data. Unlike relational database management models, Big Data’s idea is to manage all formats of data in the structured and unstructured form securely and consistently.
Unlike kilobytes and gigabytes, nowadays we are referring to data in terms of petabytes and terabytes which needed to be handled effectively. The global giants like Facebook, Amazon, LinkedIn, etc. are producing data in high standards daily and the major challenge in database management is to handle data from various sources in various formats and ensure consistency in the database.
Big data takes up the enterprise database scenario as an all-inclusive solution for the problem of maintaining solid consistency. The advancement of big data rightly supplemented the organizational needs to handle huge volumes of data, live streaming, or it and handling various data formats in terms of symbolic, numerical, video, audio, mail data, etc. irrespective of the exponential growth in data volume.
The latest situation is like there is a vast range of services which are out there at the disposal of the users over the web. One can access any website for gathering any information from anywhere in the world. So, the data provided by the web users has to be stored in a way to make it accessible anytime from anywhere in the desired mode of easy interpretation.
Why Big Data Database Testing?
Big Data database testing aims at ensuring the quality of data and also to assess the efficacy of the data mining process. Even though big data offers solutions for many of the enterprise problems related to data management, it is a big challenge to deal with big data effectively. This is primarily because of the sheer volume of data to be handled, along with its velocity of creation and the varying data structures to accommodate.
One major challenge in handling big data challenge is the unstructured format of data in big data applications. The traditional mode of data testing in RDBMS may not work ideally in big data database testing, but it has to be a well-defined and matured process to ensure accuracy.
A study conducted by RemoteDBA.com had shown that the poor quality of database applications ends up in wasting almost 14% of the enterprise revenue. Another study among the big data platform developers had shown that nearly about 20% of them point out data quality as the biggest problem which may adversely affect consistency and performance.
Unlike the structured data format, the unstructured data loads will not have a definite model to define. Such data is now so common among the social media applications like Facebook and Twitter and also data inflow from chat and email applications, audio and video files, call records, etc. These are raw human-generated data, which cannot be brought under a well-defined, structured format. So, the need is for massive and fast-growing data volumes.
Big data database testing is, in fact, the process of data testing for the integrity of processing so that the enterprises can verify their data and make use of it for analysis. Big data puts for bigger computing challenges with the massive volume datasets in a wide range of formats. Organizations now largely trust on business intelligence based on big data, so data testing also becomes crucial.
Testing of unstructured data
Testing of both structured as well as unstructured data share the same objectives as to:
- Validate the data quality, and
- Validate the efficacy of data processes.
Even though some of the testers may use ETL principles to described unstructured data testing, testing tools for this are totally different. Unstructured data may not be a part of the conventional relational databases. So, automation of unstructured data becomes a vital requirement. The tools used for testing unstructured big data sets are also complex, and the process is too complicated.
Steps in big data database testing
Step #1: Validating the Staging
Validating the data staging primarily starts with bigger data cluster, i.e., Hadoop (cloud or on-premise). Testers pull the unstructured data into test from its source and use the tools to compare the staged data to the source data.
Step #2: Validation of testing rules
In a typical Hadoop environment (on-premise or cloud), this process validates MapReduce transformations for the unstructured data sets. This approach to big data database testing proves whether the set business rules which are used to aggregate or segregate the data are properly working. The big data test is run node-by-node to check the efficacy of the business logic of each tested node.
Step #3: Validation of output
This step is used to validate the tested data and the process involved. It can verify that the step #2 testing is successful in terms of applying the business rules and also that the tested workload can retrain the integrity of data and there is no data corruption due to business logic.
On completion of these big data database testing steps, the big data tester can then move the tested and verified data into the storage systems or delete it from testing cluster. The whole process requires significant automation to handle massive volumes of data. Moreover, big data database testing is an expert task even when we use all advanced automated toolsets.