10 Ways Why Big Data Testing Is Beneficial For Digital Enterprises

When it comes to data, it means everything around us. From the web to conversations, we make with our friends on social media, everything eventually turns around to be information. Most importantly, all this data we browse and share becomes an asset for the business and research giants who make the most out of the available information through their analytical capabilities.   

Big Data is a cluster of all this information and has the potential to reveal valuable data that can be analyzed to drive the business goals or expanding the scope of certain services. Moreover, Big Data is responsible for feeding the current business environment, allowing MNCs to personalize their business offerings to the customers and have maximum value out of their sustainability objectives. And the business benefits of Big Data have all been quickly recognized, pushing the industry to grow by 103 Billion USD from 2011 to 2027, doubling its expected market growth in 2018.    In this blog, we will help you dive through every single detail associated with Big Data testing while giving you a tour of the benefits associated, the challenges involved, and the tools that can help streamline the goals associated with Big Data implementation.   

Let's begin!   

What is Big Data Testing?    

Big data, as from its name, is a collection of data on the web. It contains information related to web browsing, eCommerce, bank transactions, credit cards, wallets, social media, and any other information or task that you perform using the internet. However, different business organizations have their own set of requirements and therefore have their own collection of data which can be relational data, text-based information, or anything that defines customers or business plans.   

Since mining or collection of data needs extensive research and filtration, Big Data testing is the technique to examine big data applications. It is all about creating advanced computing systems that can handle enormous data or any software technology that could differentiate information for structures. In a nutshell, Big Data testing helps to intensify the creation, recovery, stockpiling, and analysis of information by advanced tools meeting variety, velocity, and volume goals.  

 How Is Big Data Testing Beneficial for Enterprises?   

Since we have come across understanding What is Big Data testing, most of the people who are new to the industry might have questions on how this data helps enterprises? Or What is the exact Big Data Testing Strategy that works for business?   

Using Big Data in business is more of a research-based approach that helps any organization to identify possible opportunities for sales and profit. Though Several companies applying data analytics to run depth analysis for their big data, many times, they fail to achieve the desired objective out of it due to faulty data structure, complex algorithms. Nevertheless, using the perfect Big Data Testing strategy in business can be beneficial to business in so many different ways. These include:

1. Reduces Downtime  

There are many applications that run on the data. In case of bad data, the effectiveness and the performance of the application can be affected. Deployment of Big Data applications revolving around predictive analytics, organizations might face throng. Therefore, testing needs to be done comprehensively to avoid glitches during deployment. It helps to improve data quality and related processes of the application, which further reduces the overall Downtime.   

2. Scale Data Sets  

At the beginning of any application development, it starts from the small data sets and gradually shifts to the larger ones. Applications based on smaller data sets work well. But what if the results get changed with the different data sets? Then there is a chance of failure of the application. To avoid such problems, enterprises adding a testing process as an integral part of their application lifecycle to ensure that the performance does not get affected by small or big changes in data sets.   

3. Lessens Threat to Data Quality  

Every organization wants that data should be valid, precise, consistent, and unique. If it lacks any of the above points, then there are some chances of threat on quality of data only if the rigorous testing of data can save data from becoming degradable and redundant.   

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4. Ensures Reliability and Effectiveness  

The process of collecting data from different data sources can get chances of inaccurate and unreliable data. Faulty and inaccurate data increase the risk of failure if the applications are running with real-time data. Big data testing checks the data from its root to end, which includes verification of data layers, components, and logic. This helps ensure the reliability and effectiveness of data.   

5. Validates Real-time Data  

Big data applications use live data, there is a need for some filtering, sorting, and analysis to ensure that the captured data is valid and useful. For these scenarios, performance testing of the data ensures that the application processes accurate data in real-time.   

6. Provides Data Security and Authenticity  

Security and authenticity are the extreme importance for the enterprises those deal with the client application and host their data on their server. To maintain security and confidentiality, they have to perform big data testing at different levels to avoid a security breach.   

7. Issues with the digitization of information  

Every enterprise has data or documents in paper format. As they need to convert those to digital forms, it is important to adequately test the data to ensure information isn't lost or corrupted.   
With adequate testing, enterprises can avoid the threat of data getting lost or corrupted.   

8. Optimize Processes  

Big Data and predictive analytics are the backbones of several processes. Performing big data testing can be of great help to ensure all the data used by these processes are clean, accurate which helps to avoid loopholes.   

9. Improves Return on Investment  

Enterprises need to be competitive in the Big Data and Predictive Analytics strategy. Adding testing as a mandatory activity before any analysis and processing will ensure that the enterprises use accurate data, which helps obtain the best output.   

10. Ensures Consistency  

Enterprises use a variety of applications that uses different data sets, which can be the cause of data inconsistencies. The results acquired over time with Big Data applications and predictive analytics to be inconsistent, it becomes a case of hit or miss for the organization. Testing allows them to determine variability accurately and eliminates uncertainty.  

All in all, the future of every business organization entirely depends on the available quality of information. However, meeting business goals needs corrective actions to perform the desired functions and make way for decisions that are beneficial.   

Thus, big data testing is a vital practice to follow for any business entity that needs to make the most out of the information. Besides, there are certain challenges involved in the process of Big Data testing that must be accommodated well in the implementation plan to drive productive results.  

Big Data Testing Challenges   

Since the process of yielding information for Big Data needs extensive structuring, there are certain challenges involved in the process. Here we have managed to define all the common challenges and their solutions when it comes to Big Data testing:  

Incomplete or Heterogeneous Data  

Since modern businesses believe data is vital to lead their day-to-day operations, it often leads to an extensive collection of information which sometimes leads to testing issues when done manually.  

However, overcoming such a situation needs a smarter approach to testing, I.e., the use of automation testing in big data testing strategy as automation could help testers to validate data for its quality and get rid of anything that negates the purpose.  

Scalability Challenge  

Another major challenge that testers might encounter working on voluminous data is that most Big Data software is designed to store heaps of information but nothing related to advanced accessibility, networking, and processing during the extreme workload.  

Nevertheless, the developers and testing teams could accommodate practices like Clustering or Data Partitioning in order to have an equal distribution of data for different nodes and creating parallelism through data partitioning right within the CPU.  

Test Data Management  

The third and one of the most significant challenges often experienced by testers working on Big Data applications include test data management. This usually happens when QA testers are not able to understand the test data beyond anything that involves tools like migration, processing, and storage.   

However, overcoming such concerns need QA teams to collaborate well with the developers understanding the entire process of data extraction and filtration using algorithms. Besides, QA engineers can be trained to work with big data automation tools and manual practices to ensure effective management of test data.  

  Big Data Testing Tools   

Since aligning with Big Data Testing for QA testers could turn to be a challenge at times, the entire task of data management to validation becomes easy when the right tools are available. Here we have a list of top-rated big data testing tools that you can consider for working on your next big data test project:  

• Cassandra  

Cassandra is an amazing tool used by the giants of the big data industry. The best part is, it is free and open-source while brings you the ease of handling enormous data available on commodity servers. Cassandra can be put to use when you need help with automation replication, linear scalability, and ensure not even a single point of failure.  

• Cloudera   

Also known as CDH, Cloudera is a great tool for enterprise-level deployments. It is an open-source tool that brings you Apache Hadoop, Apache Impala, and Apache Spart under its free platform distribution feature. It is easy to use and helps you align with your security requirements when you need to collect, process, and manage a large amount of data.   

• Hadoop  

When it comes to the choice of data scientists, Hadoop is a tool that meets all the tech stack requirements. Just like others, it is an open-source tool that can handle massive data and perform any processing tasks like Data crunching with efficiency. However, using Hadoop needs QA teams and testers to have a good knowledge of Java in order to meet the performance testing objectives.  

• HPCC  

HPCC or High-Performance Computing Cluster is a free tool that brings you a whole package of big data application solutions. With high scalability benefits, HPCC offers supercomputing advantages that help to work on data, pipeline, and system parallelism. However, working on HPCC need testers to have knowledge of C++ and ECL.  

• Storm  

Another open-source tool that can help you process unstructured data in real-time with compatibility across all programming languages. Storm is a fault-proof solution that offers extensive reliability in processing data. Above all, it is a cross-platform tool that allows continuous computation, log processing, machine learning, and real-time analytics.  

The Crux  

Concluding it all, Big Data testing is pivotal for any business that needs to progress on strategic grounds. From making the right decisions to using information for sales planning and customer targeting, the use of big data applications and software need to offer all the functionalities that take to drive maximum value. And the only way to attain such goals is to work on a thoughtful big data testing strategy that works well for the development planning, implementation, tools, and anything that can help cut off the challenges on the way.   

All The best!  

Need help working on your big data testing strategy or trying implementing that big data application for your business benefits? Let the experts at BugRaptors be the helping hand you need.   

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