A Comprehensive Guide for Big Data Testing: Challenges, Tools, Applications
By Rohit Sharma
Updated on Feb 24, 2025 | 11 min read | 6.0k views
Share:
For working professionals
For fresh graduates
More
By Rohit Sharma
Updated on Feb 24, 2025 | 11 min read | 6.0k views
Share:
Table of Contents
Previously, all data was preserved in a tabular format, also known as structured data. Now, the data is increasing exponentially as every individual wants to stay connected and share things they care about.
Now, the internet has more unstructured data than structured data. It will increase in scale in this new decade because of IoT, self-driving cars, artificial intelligence, online banking, online shopping, etc. Currently, only about 20% of data is structured, and 80% of data is unstructured.
Data is generated by almost every action performed on the internet. For example, when a user checks out their social media feed, data is generated. Liking a post, performing a Google search, sending a message, taking a cab—all of these involve data generation. All modern businesses use the power of data to scale and grow and become more customer-centric.
To get insights or information from the data, we need to design a system. Here, we will talk about Big Data testing, some of the challenges faced by organizations, ways to improve Big Data testing, some strategies for testing, ways to automate your testing process and tools, and the tech stacks to perform Big Data software testing.
Testing with Big Data has to be included in an organization’s development cycle. As the businesses are going global, there are many customers, and their data gets generated, which needs proper control; otherwise, it becomes useless. With social media’s help, all the local to global businesses are trying their best to acquire customers.
All successful teams that have introduced Big Data have taken specific steps to get the world’s best products and systems as in this instant world; everything has to be served quickly. If it takes more time, then you are out of the business.
For making a perfect product that is market-ready, Big Data testing is essential, just like QA testing for software development. You can, too, start with QA testing for Big Data by following up on this article.
Traditional QA testing doesn’t align with Big Data. Testing with Big Data is a unique process. For creating a well-performing system, the Big Data QA testing method is used, which is also known as ‘Big Data testing’. All the new software like Hadoop, Cassandra, etc., are required to derive insights from vast amounts of data and use them for testing purposes.
Some types and techniques to start testing with Big Data are described below.
There are numerous challenges with Big Data testing, some of which are listed below, as most of the data is unstructured. It can lead to more heterogeneous data. However, following a proper technique can mitigate many hurdles and help businesses grow. Learn more about the challenges of big data.
There are various tools available for Big Data QA testers. Some of the best tools are listed here to help develop business operations informed by Big Data.
Hadoop is a favourite of all, especially data scientists. Hadoop handles multiple tasks with great processing power and precision. It can store massive amounts of data along with various data-types.
Big tech firms use Cassandra for QA testing with Big Data. It is free and open-source software. It can handle various Big Data operations like automation and linear data handling and is a very reliable system.
A storm is a cross-platform tool used to handle various operations by integrating different third-party software, making it easier to work. A storm is a real-time software used for Big Data testing.
HPCC is a High-Performance Computing Cluster, and it is a free tool. It features a scalable platform for supercomputing and supports all three parallelisms (i.e., system parallelism, pipeline parallelism and data parallelism). It requires an understanding of C++ and ECL.
In the ever-evolving landscape of big data testing, several emerging trends have gained prominence, shaping how organizations use big data testing tools and big data testing strategy to test their vast and complex data environments. These trends leverage advancements in technology and methodologies to enhance the efficiency and effectiveness of big data testing processes. Let’s explore some of these emerging trends.
Performance optimization ensures that big data systems deliver results within acceptable timeframes and meet the growing demands of data processing and analytics. Let’s explore some performance optimization strategies employed in big data testing.
Thus, all the processes are interconnected and can produce a great outcome if performed together in a link. It requires time to learn initially, but in the long run, it reduces the significant time plus increases the team’s efficiency, and helps all the businesses grow and provide real value.
The domain of Big Data is relatively new as more data has been generated in the last 4-5 years, so there are many challenges and opportunities to grow and make a significant impact with your contribution. Check out this Big Data course to learn about Big Data testing and be market-ready with your skills and projects.
If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.
Check our other Software Engineering Courses at upGrad.
Get Free Consultation
By submitting, I accept the T&C and
Privacy Policy
Start Your Career in Data Science Today
Top Resources