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The relationship of Big Data in Quality Assurance Testing Services

It’s critical for a business to adapt to the dynamic changes in the technology and market evolution, with regular updates of software applications. The success of an app isn’t solely dependent on development but on quality as well. Regardless of how intuitively a software is designed and the functionalities that are made simple, if there are technical hitches, then it will hinder usability. That is why it is mandatory for any product to undergo software testing. Quality assurance testing assures smooth operation, flawless user experience and meet overall quality aspects of the system.

Computer software examination, or software QA testing is provided by software development service providers. In the process, quality assurance is an integral part of each and every project undertaken. Effective and reliable service providers follow industry standard testing methods to help clients meet their quality assurance goals. QA experts are proficient in numerous testing technologies tools, standards and platforms, which enable them to apply integrated testing services as well as the best solutions.

Data is the lifeline in any organization and is growing bigger with each passing day. Back in 2011, experts predicted that Big Data would be the next frontier of competition, productivity and innovation. Nowadays, businesses face data challenges when it comes to volume, sources and variety. Structured business data is supplemented with unstructured data as well as semi-structured data from social media and other parties.

There are numerous advantages of Big Data mining. However, separation of the required data from junk isn’t easy. The Quality Assurance team should overcome different challenges in testing of such huge data.

Some include the following:

1. Big volume and heterogeneity. Testing a large volume of data is in itself the biggest challenge. Ten years ago, a pool of data of 10 million records was considered gigantic. Nowadays, businesses should store Exabyte or Petabyte data extracted from different offline and online sources, to perform their daily business. Testers have to audit such voluminous data to ascertain that they fit business purposes.

2. Understanding data. For an effective testing strategy, testers need to monitor and validate the 4Vs continuously. These are volume, variety, velocity and value. Understanding data and its impact on business is the real challenged faced by any big data tester. It isn’t easy measuring the testing efforts and strategy without the right knowledge of the available data’s nature.

3. Dealing with emotions and sentiments. In a big data system, unstructured data that’s drawn from sources like text documents, tweets and social media posts supplement data feed. The biggest challenge that testers face while dealing with unstructured data is the sentiment that’s attached to it. Testers should capture their sentiments and transform them to insights for further business analysis and decision making.

4. Lack of coordination and technical expertise. Technology is growing, and everyone struggles to understand the big data processing algorithm. Testers of huge data should understand the components of the ecosystem thoroughly. Nowadays, testers should understand that they should think beyond the regular parameters of manual and automated testing.

The following figure provides high level of overviews of phases in testing data applications. The tests include.

1. Data staging validation. The first step to big data testing also referred as pre-Hadoop stage involves a validation process. Data from different sources, such as weblogs, RDBMS, social media and others must be validated to ensure that correct data is pulled into the system. Comparing source data with data pushed into Hadoop ensures that they match. Tools such as Datameer and Talend could be used for data staging validation.

2. Validation of MapReduce. In this step, the tester verifies business logic validation on each node and validate them after running against numerous nodes, making sure that MapReduce process works correctly. Data segregation and aggregation rules are implemented on the data.

3. Output the validation phase. The third or final stage of Big Data testing is output validation process. The output data files are acquired and ready to be moved to enterprise data warehouse or any other system that’s based on the requirement.

4. Architecture Testing. Hadoop processes very big data volumes and highly resource intensive. Thus, architectural testing is paramount to ensure success of any big data project. Improper or poorly designed system could lead to degradation of performance and the system could fail to meet the requirement.

5. Performance testing. Performance testing for big data app involves testing of big data volumes for both structured and unstructured data. Moreover, it requires a certain testing approach to test such massive data.

Big Data, all in all has a lot of prominence in businesses these days. If the right test strategies are embraced and the best practices are followed, defects could be determined in the early stages of the overall testing expenses could be reduced while achieving quality at speed.

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