Datafloq

Data and Technology Insights

Big Data Testing Understanding the Complex World of Data Testing Strategy

/ 4 min read.
Datafloq AI Score
floq.to/6oqTA

Big enterprises with a large footprint on the World Wide Web through numerous touchpoints must deal with a humongous quantum of data generated every second. Traditional data storage and processing methods or tools are seemingly inadequate to deal with such data volumes in real-time.

Hence, such enterprises need a big data testing strategy to process data and derive insights for making impactful business decisions. Automation plays an important role in the whole process, as both structured and unstructured data need to be stored, analyzed, and processed in real-time.

Further, since big data needs to be migrated to the right processes or areas for generating business outcomes, it should be subjected to rigorous migration testing to check whether the final data received at various nodes is accurate, complete, and valuable.

What is big data?

According to Gartner, big data is defined as a high volume and diverse set of information assets generated at high speeds, necessitating quick, innovative, automated, and cost-effective processing to gain greater insights into the organization and its stakeholders in order to make quick and accurate decisions.

As more organizations are transforming their processes and expanding their footprint on the internet using technologies such as IoT, AI & ML, among others, the big data industry is expanding rapidly. According to estimates, the industry will be worth $77 billion by 2023, and the quantum of data generated every second in the financial sector will increase by 700% in 2021 (Source: sigmacomputing.com)

Why do traditional databases not suffice to handle big data?

Due to its sheer volume, unstructured format, and variety, traditional databases are not capable of handling big data. The other reasons are mentioned below:

  • Conventional relational databases such as SQL, MySQL, and Oracle cannot handle the predominant unstructured format of big data.
  • RDBMS cannot be used to store or handle big data as they need the data to be stored in a row and column format.
  • Conventional databases will not be able to handle such a huge volume of data generated at high speeds.
  • Big data constitutes different types, namely, videos, images, text, numerals, presentations, and many others.

What is big data testing?

The huge volumes of data collected from various sources are needed to be stored, processed, analyzed, and retrieved To determine their characteristics and usage, these data are subjected to various testing procedures, such as data analytics testing. The primary characteristics can be defined in terms of volume, velocity, veracity, variety, and value.

Here, volume refers to the size of data, velocity is about the speed at which the data is generated and received, veracity is about the trustworthiness of data, variety is about the types of data generated and received, and value is about the idea of how big data can be put into use for the benefit of the business.

Key components of a big data testing strategy

The key components of testing big data applications are as follows:

Interested in what the future will bring? Download our 2023 Technology Trends eBook for free.

Consent

Data validation: In this phase, the collected data is validated for accuracy, completeness, and non-corruptibility by passing it through a Hadoop Distributed File System (HDFS). Here, the big data is partitioned and validated using tools such as Informatica, Datameer, or Talent. From here, the validated data is moved into the next stage of HDFS.

Process validation: Also called Business Logic Validation, the process involves the checking of business logic for various nodes. Here, the tester verifies the process as well as the key value pair generation. This phase marks the completion of the data validation phase.

Output validation: This phase is about checking big data for distortions or corruption by loading it downstream. The output files generated in the process are moved to the Enterprise Data Warehouse or EDW.

What are the benefits of a big data case study?

By reviewing the case study of big data testing, enterprises can gain the following benefits:

Decision making: Any data-driven decision can be relied upon and help an organization to steer the right course of action. Using data and analytics, an organization can derive benefits such as better relations with customers, a better understanding of risks, better performance, and better driving of strategic initiatives, among others.

Data accuracy: Most data collected as a part of big data automation testing is unstructured and needs to be validated and analyzed for accuracy. This helps an organization owning such data to identify any vulnerabilities and deliver better results.

Increase revenues: The analysis of big data can enable better management of customer relationships, resulting in addressing the concerns of customers. This can deliver superior customer experiences and increased product sales and revenues.

Conclusion

Implementing a big data testing strategy along with migration testing has become the need of the hour for big enterprises dealing with huge volumes of data. With such testing, they are likely to gain additional benefits such as seamless integration, reduced cost of quality and time to market, minimized risks, and enhanced business performance.

About Hemanth Kumar

Hemanth Kumar Yamjala has 10+ years of experience in IT Services, predominantly Marketing, Branding, specializing in Digital. Currently a part of the marketing for Cigniti Technologies with functions such as leveraging digital marketing channels for lead generation and promotion.