Top Testing Trends In The World Of Big Data


The world has witnessed a data explosion over the past decade. Many businesses were facing problems with this data blow-up. The need for a solution to this resulted in the development of the concept of Big Data.

Big Data has three defining traits that are Variety, Velocity and Volume. Today, many different applications used in sectors like healthcare, finance, telecommunications, social media etc. depend extensively on Big Data.

This exponential growth in the use of Big Data results from an increase in the usage of IoT devices. Such is the popularity of Big Data that surveys predict an increase in the global value of its market from 138.9 billion USD in the year 2020 to 229.4 billion USD by 2025.

Defining Big Data

Big Data apps help to search, capture, store and analyse data, but they differ from traditional client-server apps and websites both in their nature and involved complexities, which testers need to keep in mind both functionally and in any automation testing services that are taken up.

The major steps involved in Big Data app testing after the QA process has been developed and defined are:

  • Preparing the sample and actual data for conducting the Big Data app tests
  • Testing of the app’s components individually
  • Then testing the Big Data app as a whole and,
  • Performing reliability and performance testing

Emerging Big Data testing trends

The latest trends emerging in Big Data testing are:

  • Testing for improved efficiency with the integration of live data: Big Data apps live on real-time analysis and LIVE data feeds. However, the sheer variety of source data increases the complexities associated with LIVE integration. Since data integration testing is done to verify if data movement is as expected, the used data quality needs to be thoroughly tested right from its source to its destination. Only then will it be possible to come up with an optimised analysis of the same.
  • Using instant app deployment testing to reduce downtime: Big Data apps focus primarily on predictive analytics and hence are highly dependent on collection and deployment of data. Since the results obtained from Big Data help businesses make key decisions, its instant hassle-free deployment is extremely crucial for establishing business dynamics. This makes it mandatory to test data and apps before their LIVE deployment.
  • Scalability Testing: The sheer volume of data generated increases the importance of ensuring scalability of Big Data apps. Proper data samples need to be used to ensure that these apps support the enormous data load they have to deal with. All scalability issues can be done away with by simply using smart data samples for testing the app framework at important moments. The need is to ensure Big Data app scalability without compromising on its performance.
  • Security Testing: This is another popular emerging trend in Big Data app testing. Big Data comes from a variety of sources. Thus ensuring the security and confidentiality of this data when developing the apps is an important feature. To stop public exposure of any data processed on any Big Data app, it needs to undergo security testing. Big Data apps will suffer badly if hacked and hence the application layers are tested using different types of testing mechanisms.
  • Functional Testing: This includes the validation of quite a few processes to ensure accuracy and quality of data like:
    • MapReduce process validation that helps to divide an app into small fragments based on multiple nodes arranged in a cluster,
    • Structured and unstructured data validation,
    • Data storage validation using automation testing services,
    • Validation of data processing etc.

Functional testing, thus, helps to validate app functioning before its storage within the data warehouse where it is repeatedly validated to keep it aligned with the app’s volume data processing.

  • Non-Functional Testing: This includes app performance and failover testing to reduce bottlenecks.
    • App Performance Testing: All apps must provide superlative performance. Performance testing becomes even more critical for Big Data apps since:
      • They use LIVE data and
      • Have to provide analytical insights for such complex data.

Performance testing also plays an important role in ensuring the scalability of Big Data apps. Some critical performance areas that are particularly susceptible to performance issues are:

      • Redundant shuffle inequalities,
      • Splitting and sorting of inputs,
      • Moving aggregation computations to enable process reductions etc.

However, these performance issues can be eliminated by:

The performance testing of Big Data apps include:

      • Ascertaining the response time, the maximum processing capacity of the system and the data capacity size required when a maximum number of users are online,
      • Testing of time-sensitive loads,
      • Testing to check whether performance parameters like hardware, application codes etc., are optimised,
      • Testing of the app infrastructure,
      • Testing the app’s capacity for data processing,
      • Testing the capacity of the apps data transmission capabilities.
    • Failover Testing: It deals with finding out how resource allocation is done by the system when one of its components fails. It also helps to validate the system’s ability to back-up operations when the server crashes.

Additionally, failover testing also checks to see if the system can handle extra hardware resources during critical times.

For example, Big Data apps working on the Hadoop architecture consist of the name node and several attached data nodes. These attached nodes are hosted on and connected to different server machines.

Thus this architecture always remains susceptible to network, data or name node failure. Failover testing helps validate the recovery method used and checks to see if data processing continues seamlessly even while switching over to other different nodes.

  • Functional Consistency Testing: The importance of Big Data stems from the fact that it can access varied data sets. An enterprise can produce almost unlimited possibilities especially when it possesses the right knowledge for it.

However, if the Big Data app results obtained over a certain time become inconsistent with the projecting analytics, the whole labour goes for a toss. Performing functional consistency testing correctly helps apps remove uncertainty by accurately defining the variable.

  • Automation Testing: Big Data apps undergo periodic database updations. Thus, its regression test suite is used multiple times because of these periodic updations. Consequently, automation testing services need to test this regression test suite on priority.

These are the major dimensions of the emergent Big Data app testing trends. With Big Data well on its way to becoming a leading domain of technology all set to define the future and the ability of the human race to take growth trend defining decisions, the importance of Big Data testing increases further.

About the Author

QA InfoTech

QA InfoTech

Established in 2003, with less than five testing experts, QA InfoTech has grown leaps and bounds with three QA Centers of Excellence globally; two of which are located in the hub of IT activity in India, Noida, and the other, our affiliate QA InfoTech Inc Michigan USA. In 2010 and 2011, QA InfoTech has been ranked in the top 100 places to work for in India.

Related Posts