Blog: Data Insights

A Complete Guide to Big Data Testing

December 16, 2020 - by Synoptek

As the amount of data that’s being generated grows with each passing day, the use of big data applications to manage this growing volume of data is also swelling. Analysts expect the global big data market to grow from $138.9 billion in 2020 to $229.4 billion by 2025. Given that traditional computing techniques do not have the ability to process these large data sets, big data applications pave the way for efficient and timely management of data pertaining to customers, processes, and competition.

When done right, data testing can help businesses in real-time fraud detection, competitive analysis, sentiment analysis, and traffic management. But the never-ending need for the creation, storage, retrieval, and analysis of colossal volumes of data means organizations need to rely on precision tools, remarkable frameworks, and brilliant strategies, which brings big data testing into the spotlight.

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What is Big Data Testing?

Big Data testing entails testing of big data applications to ensure they perform as expected. It involves examining and validating the functionality of the Big Data applications that traditional storage systems cannot handle. Since these applications deal with huge amounts of data, testing requires the use of special tools and techniques – that take testing to a whole new level.

Why is Big Data Testing Important?

Big data applications deal with critical data that gets generated from sensors, mobile devices, cameras, wireless networks, and other IoT devices at an extremely fast pace. Testing these applications means verifying their data processing capabilities – across performance and functions. Ensuring big data applications perform as intended is important because:

  • The quality, consistency, and accuracy of data that gets fed into them directly impacts analysis results. Therefore, it is critical for this input data to be thoroughly tested, so the output is accurate and reliable.
  • Their data processing capability is directly linked to business decision-making; since decisions are directly made based on the analysis that these applications carry out, any wrong analysis result can impact the decisions being made.
  • They constantly process information pertaining to day-to-day activities. Due to the fact that the results they generate set the foundation of business strategies and long-term goals, the applications must deliver dependable results.
  • Their outputs can help businesses make decisions that can identify anomalies, improve customer satisfaction, minimize losses, increase revenues, and outdo competition.

What are the Benefits of Big Data Testing?

Now that you understand why big data testing is important, let’s dig a little deeper into why it is beneficial. There are plenty of benefits that big data testing can bring to an organization, and you might not be aware of them all. Let’s take a look at few benefits:

  1. Quality, Accurate Data – Having a lot of data at your disposal poses no benefit if it is not accurate. After all, unstructured data is essentially useless. With big data testing, however, you’ll be able to better identify the quality, accurate data you need to strengthen your strategy.
  2. Improved Decision Making – When you have a wealth of data around you, you can make much more informed decisions. While some managers may forgo using such data insights to make better decisions and stick to what they think they know works best, it is truly the way to go. You will be able to see a larger, more clear picture, and pinpoint what your business needs.
  3. Boost Revenue – When your big data is poor quality, you’re losing out on more than just better business processes. In fact, you’re missing out on significant revenue. When you have big data testing in place to identify poor quality data, you will be able to minimize potential losses and boost revenue.

How is Big Data Testing Different?

Unlike testing of normal applications where a significant time is spent on testing the performance and functionality of different software components, a large portion of effort in big data testing goes in validating data:

  • Data that comes from various data sources is first validated for accuracy, duplication, consistency, and data completeness.
  • Followed by a second round of validation after the data is processed by the big data application.
  • And again, by another round of validation to verify if the output data is correctly stored in the data warehouse and correctly used by the corresponding BI or AI application.

Why is Big Data Testing Challenging?

Big data applications need to process large amounts of data in a short period of time; therefore, they need to be able to function properly – quickly, smoothly, and without error – while keeping up with the required levels of performance and security. Testing big data applications brings with it its own set of challenges:

  • Big data applications usually deal with a variety of structured and unstructured data including images, videos, text, and audio.
  • Since many applications are used for real-time monitoring, they need to be able to carry out parallel processing of data in real-time and generate results almost instantly.
  • The presence of unstructured data means traditional relational databases cannot be used to store this data in row and column formats.
  • Because big data applications cannot afford performance bottlenecks, the underlying architecture needs to be constantly monitored for issues.
  • The velocity at which big data is created means applications need to be tested for their ability to process humongous amounts of varying data – every second.

Big Data Testing Tips and Best Practices

Big Data testing requires QA engineers to test and verify terabytes of data – right from when the data is fed, processed, and stored. Given the variety, velocity, and volume of data that needs to be processed, Big Data testing demands a sophisticated level of testing skills as data needs to be processed quickly and accurately.

In order to get the best results from Big Data testing initiatives, it is important to use tools like Hadoop to test and verify the ETL process as well as automation techniques to accelerate the speed and accuracy of testing. That being said, here are some tips and best practices:

  • Begin the testing project by checking the quality of input data for accuracy, consistency, compliance, and completeness.
  • Carry out data integration testing to ensure data coming from different sources is properly integrated into a single repository.
  • Make sure you have enough space and CPU capacity for storing, processing, and validatin7 massive volumes of data.
  • Test the application to see how fast it can get through the data at every stage and how fast the data is being processed.
  • Check the application for throughput and memory/CPU utilization to identify and resolve bottlenecks and improve the time taken for analysis.
  • Given that big data applications are extremely prone to producing ambiguous results, carry out constant failover tests to check and validate fault tolerance.
  • Carry out business logic validation across different nodes and validate the process to ensure the application performs as intended.
  • Once the output is generated, check for data integrity, accuracy, and corruption.

The Right Testing for the Right Outcomes

Advancements in technology have caused massive amounts of data to be generated every second – which needs to be constantly processed for accurate, data-driven business decision-making. Big data applications have a major role to play in processing these large volumes of data, but ensuring these applications carry out accurate processing means they must constantly be tested. Big data testing allows for testers to test the performance and functionality of these applications and ensure data that is fed, processed, generated, and stored meets quality, accuracy, consistency, and compliance requirements.

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