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A Comprehensive Guide for Big Data Testing: Challenges, Tools, Applications

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21st Jun, 2023
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A Comprehensive Guide for Big Data Testing: Challenges, Tools, Applications


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.

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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.

Big Data Testing

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.

  • Functional: Front-end application testing helps with data validation. It helps to determine the actual difference between the expected output and the actual output. Front-end testing always helps with knowing the tech stack in and out and finding bugs.
  • Performance: Automation is key in Big Data as an increase in data will lead to a lot of work if not automated. This testing involves checking all the features under various conditions and creating proper products or systems for large-scale use. Performance testing is one of the key elements as it helps to identify bugs and obtain all the relevant information from a set of Big Data.
  • Data Ingestion: The data ingestion technique is used to extract the Big Data’s relevant data and verify whether the data extracted is correct and useful.
  • Data Processing: Here, the data automation tools help determine if all the data generated from the data ingestion technique is aligned with the business model. The data must be informative for the business.
  • Data Storage: Now, it’s important to ensure the information derived from the Big Data is appropriately stored in a data warehouse. It is verified by getting the output from the data warehouses. Comparisons are made between data stored in the warehouse and the system’s data to generate the required output.
  • Data Migration: The word ‘migration’ refers to the data which is migrated or moved to a new server. In some situations, if the tech stack is changed in the near future, then we need to use this Big Data QA testing method known as ‘data migration testing’. It helps assess how the data is retained and adapt to the new system with no loss and less downtime.

Challenges Faced in Big Data Testing

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.

  • Incomplete and Heterogeneous Data: The data is not proper as most of it is unstructured. Also, due to various sets of users’ data being available, the data tends to be incomplete. It creates a considerable challenge in analyzing the data and developing new approaches to deal with it. Incomplete and heterogeneous data can lead to difficulties in getting the required information out of the data.
  • High Scalability: All the data gathered are from various sources, so scalability is always an essential factor in Big Data testing.
  • Test Data Management: All the data generated after the test has to be tested and stored well in the system to make it useful. If the test data is not managed correctly, it will lead to data loss and the loss of useful information derived from the data, which is essential for businesses.

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Tools Used for Big Data Testing

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.

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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.

Emerging Trends in Big Data Testing

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.

  • One significant trend is adopting machine learning (ML) for test automation in big data testing. ML algorithms can analyze large volumes of test data, identify patterns, and generate automated test scripts, reducing manual effort and increasing test coverage. By leveraging ML techniques, organizations can improve the speed and accuracy of their big data testing efforts.
  • Another emerging trend is containerization for developing and managing test environments. Containers provide a lightweight, portable method of packaging and installing programs and their dependencies. Organizations may simply build up and duplicate test environments by employing containerization technologies such as Docker or Kubernetes, resulting in increased agility, scalability, and consistency in huge data testing.
  • Furthermore, incorporating artificial intelligence (AI) in huge data testing has gained popularity. AI algorithms can review massive volumes of testing data, identify abnormalities, and give insights to improve test design and execution. AI-powered anomaly detection approaches may assist in spotting outliers, anomalies in data, and possible problems in real-time, enabling faster identification and resolution of issues in large data systems.

Performance Optimizations in Big Data Testing

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.

  • Parallelism is an essential aspect of large data testing performance optimization. Extensive statistics structures are intended to process and analyze enormous amounts of data in parallel across distributed computing resources. Test scenarios must be devised to emulate real-world scenarios in which information is processed concurrently, ensuring that the device can manage the workload appropriately. Organizations can find and fix bottlenecks.
  • Another way to improve overall performance is resource allocation. Big data systems rely on distributed computing frameworks such as Apache Hadoop or Apache Spark, which modify data across a cluster of devices. Optimizing usable resource allocation entails fine-tuning characteristics such as memory allocation, CPU utilization, and network bandwidth to provide the most dependable performance throughout testing. Companies may improve the performance and responsiveness of their massive data structures by effectively allocating resources.
  • Furthermore, optimizing data input and processing is critical for achieving the best overall performance. Techniques like fact partitioning, data compression, and efficient data serialization formats may significantly improve data input and processing speed and performance in huge data systems. Corporations can minimize processing instances, increase system throughput, and improve standard overall performance by optimizing information management approaches.
  • Additionally, organizations should consider load and stress testing to identify performance limitations and ensure system scalability. Load testing involves simulating high-volume data scenarios to assess system performance under heavy workloads. Stress testing involves pushing the system beyond its limits to determine the breaking point and uncover potential vulnerabilities. These tests help organizations identify areas of improvement and optimize system performance in high-demand situations.

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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.

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Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.
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Frequently Asked Questions (FAQs)

1Is Big Data testing in demand?

Big Data testing ensures that all the functionalities of a Big Data application work properly and as expected. The Big Data testing market size was valued at $20.1 billion in 2020, and it is estimated to grow at a CAGR of 8.0% during 2021-2026. The growth is mainly attributed to the adoption of advanced technologies and their adequate adoption. There is an increasing adoption of Big Data testing platforms over time. 96% of companies are definitely planning or likely to hire new staff with relevant Big Data skills, and the salaries offered are often very huge.

2Which industries use Big Data?

Big Data has become a big game-changer in various modern industries over the last few years. Most organisations have several goals for adopting Big Data projects. The primary goal for most organisations is to enhance customer experience, cost reduction, better-targeted marketing, and making existing processes more efficient. The banking and securities industry uses Big Data to curb challenges like security fraud, card fraud detection, etc. Healthcare providers have access to huge amounts of data but have been plagued by failures which are solved using Big Data. The education sector, manufacturing and natural resources, government sector, and insurance sector are certain other industries using Big Data.

3What are the advantages of Hadoop for Big Data?

Hadoop has become a familiar term and has found its prominence in today’s digital world. Hadoop framework is vital. It was created to deal with Big Data and offers many benefits. Speed is one significant advantage of Hadoop, as it lets users run complex queries in just a few seconds. Structured, semi-structured, and unstructured are different data formats which can be stored in Hadoop’s HDFS. It is also very cost-effective. Hadoop functions in a distributed environment, and one can easily add more servers.

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