Big Data Testing Services

Building software for Big Data testing

Every company aiming to utilize Big Data processing to improve its software delivery workflows and cloud infrastructure management operations must understand that while Big Data can theoretically mean ALL the data, in real life an attempt of deploying a data processing application covering all the available structured and unstructured data will mean only wasting your limited resources trying to perform the impossible task.

The first step of all Big Data testing strategies is determining whether you actually need Big Data processing to obtain the business intelligence needed, or deploying an appropriate mathematical model. Based on our experience, in quite a lot of cases, a predefined mathematical equation is enough to fill the analytical role required by your business logic. For example, financial analytics are based on historical data and an appropriate mathematical model can extrapolate them into the future.

However, there are also multiple scenarios, where you actually need Big Data applications to enable real-time Machine Learning and data processing. For example, many online businesses require some mechanisms of user authentication to ensure their customers are 18+ and are eligible to use their services. The most widespread method of user authentication is by submitting a photo quality scans of some government-issued user IDs like passports or driver licenses.

Prior to the introduction of a Machine Learning algorithm called Optical Character Recognition, a specially designated employee had to sit and manually approve every customer submission. Now, an Artificial Intelligence algorithm can be trained using large data sets of publicly available photos of human faces and examples of ID templates. After such a Big Data application is built, it can automatically compare the photo on the ID and the user’s photo and approve the application in seconds. It can also automatically parse the details like birth date, name and surname and many others if they are clearly visible on the document. This saves a ton of effort on customer validation and helps a growing business meet its business goals successfully.

However, these results can be achieved only if the Machine Learning model you use gets trained correctly. This is where a business needs Big Data testing services, as this approach allows utilizing the expertise of various data scientists and Big Data analytics experts, who will be able to design, implement and run the Big Data application for you. IT Svit is a Managed Services provider that has ample experience building various kinds of Big Data applications and delivering Big Data testing as a service.

Types of Big Data testing strategies

Thus said, there are two major parts of Big Data testing applications — for internal use and for external use. Big Data solutions that are used internally are mostly applied for providing Machine Learning automation of cloud infrastructure management, so-called predictive analytics, which leads to a self-healing infrastructure, a dream of every IT operations team.

Big Data applications intended for external use become parts of the customer-facing systems. Their place in the business workflows can be either the main moneymaker or one of the product’s killer features, but they deliver the value to the end-users all the same. These types of Big Data processing can include platforms that analyze the store’s stock and the buyer’s history of purchases to suggest cross-selling or upselling; highlight similarities and best practices in various business operations to improve corporate training and employee retention; alert of potential low sales numbers and launch one or several possible scenarios to correct the situation and provide a healthier bottom line.

Big Data applications can perform a wide variety of useful operations to deliver value for your business or to your customers — but they must be built correctly and trained using the relevant data in order to do this. Big Data testing is the methodology of preparing the data before the actual analytics can commence in earnest so that your systems don’t waste time and computing resources on processing terabytes of data you don’t really need to analyze.

• Initial data validation. Your Big Data application will process large volumes of structured and unstructured data from a variety of sources. It must undergo various checks before it is stored in the Hadoop, or it will waste valuable resources. The data should be deduplicated to remove copies; normalized to unify the representation; checked for consistency and completeness; validated by time to process the latest and most relevant data. After all of these kinds of the Big Data testing are applied, an AI algorithm must check if the processed data contains all the values and parameters required by your Machine Learning model, so it can be safely stored to the correct sector of the Hadoop file system.
• Map-reduce validation. An experienced data scientist must validate business logic for every node to ensure map-reduce works perfectly for multiple nodes. This stage of testing helps check if all data segregation rules work as intended, key-value pairs are created correctly, and data validation works well after the map-reduce process.
• Data output validation stage. Before storing the processed information to the business data warehouse you must validate the output. A specialist must ensure the transformation rules are applied correctly, the data at the destination system is consistent and compare the output data with the content of the Hadoop distributed file system.

Once this initial Big Data testing is done, the data science team must check the Big Data application for architectural consistency and performance.

Big Data software testing

The next stage of the data processing workflow involves checking the Big Data solution performance. This stage is crucial to ensure the long-term success of your Big Data project, as the underlying infrastructure must be able to scale up and down easily, work stably under a heavy workload and deliver real-time data processing. This is why it is crucial to work with experienced Big Data and DevOps professionals, who will be able to design, build and run a Hadoop cluster appropriately. 

This stage involves performance testing, failover testing, job completion time tests, data throughput, memory utilization and other kinds of system tests. Failover testing is essential, as it ensures the reserve nodes are created seamlessly should the main nodes fail, so the data processing can continue without interruptions. Below we list the key Big Data testing points:

• Data throughput. One of the most essential characteristics of a Big Data platform is the speed of data processing or velocity. The rate of message insertion into Redis, MongoDB, Cassandra and other SQL or NoSQL databases is essential for project success.
• Data processing. Run the pilot on large data sets with occupied Hadoop nodes to see how your system will perform under the maximum workload. This helps ensure the Map-reduce process is executed flawlessly on distributed Hadoop clusters.
• Component performance. Big Data applications work using a variety of components. While these must work in unison, they must also be checked individually to ensure all the components are configured correctly.

Once these tests are accomplished, your Big Data solution is ready to begin working on your project. IT Svit would be glad to assist with designing and deploying Big Data applications for you, as well as testing and running them in production. Should you need Big Data testing services — let us know, we are ready to help!