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Big Data Testing For DevOps And Agile: Are Your Installations Keeping Up?

Nastel Technologies®
October 28, 2019

DevOps – It’s a generally accepted maxim that the business community’s fascination with big data, which started in the mid-2000s, ran out of steam about five years ago. But that’s only partly true.

While the talking points have shifted to DevOps, Agile, AI and machine learning, big data never left the scene. On the contrary: It has become so widespread that no one feels the need to bring it up anymore. Today, it’s just data. To give some context, big data and business analytics solutions are expected to reach $274.3 billion worldwide by 2022, from an estimated $189.1 billion in 2019, according to International Data Corporation.

It’s no different in the domain of software testing. Big data testing plays a massive role in helping companies identify defects in real time, reduce storage costs, make smarter decisions, manage client expectations and implement new strategies. And that’s just in the short term. Big data testing can lead to greater efficiencies and increased revenue in the long run because large volumes of data can reveal patterns and trends that would be unobtainable otherwise.

I must also add that big data testing is, nevertheless, in an exploratory phase. As a fairly recent trend, QA teams around the world are still probing for best practices. Testers must consider all the indexes and abstractions in order to reveal patterns out of low-density, crude data. The sheer scalability of big data means that the application has to be continuously verified across its operative capacity — without processing the full load that comprises the enormously high data quantities.

All this is new territory for many testers. But with the snowballing effect of the number of big data applications, the demand for these skills is accelerating in parallel.

Big Data Testing Constraints In DevOps And Agile Environments

The perceived drawback of big data testing is that testers end up spending the bulk of their time validating data instead of actually testing the system. Managers see it as an antithesis in today’s climate of booming Agile frameworks and DevOps practices that enable fast change and fast release.

Simply put, testers are faced with so much data that traditional databases such as Oracle and PostgreSQL can’t handle the volume. Big data comes in a plethora of formats, comprising unstructured data and a huge range of different data (video, audio, images, etc.) which is challenging to store in the row and column setup we’re all familiar with. Plus, the velocity of large data volumes makes storing and retrieving data impossible with conventional software techniques. Just think that around 6,000 tweets are shared every second, on average, according to Internet Live Stats.

Adding on, virtual machines required for virtualization testing can be slow and unresponsive and can cause bottlenecks when running big data testing in real time. When it comes to huge datasets, testers always need to verify more data at a quicker rate. Also, they must be able to test across different platforms, which can hinder progress because each platform is made up of its own unique properties.

Even that’s not all. Automated tools aren’t capable of solving problems that randomly pop up during testing; this means that Agile and DevOps teams will need the expertise of someone with strong technical proficiency to keep testing running smoothly.

Lastly, with big data testing, testers don’t have the option of implementing a sampling strategy in order to manually test what they consider statistically justified cases. They also can’t perform exhaustive verification and test all possible data combinations present at the beginning of testing.

Tips And Tricks

How should DevOps and Agile teams reduce lengthy setup times that defer production? If you’re a manager, you can:

Invest in training. Regular testing tools require a rudimentary working knowledge, while big data testing requires a specialized set of skills.

Optimize your data life cycle management. Design testing installations so that data is restored at a continuous rate. Importing new data and cataloging old data should run without interruption, minus the requirement to create individual folders for every iteration.

Locate defects, and disable blockers. This is where performance testing comes in. It quickly processes huge data volumes and estimates the speed, scalability and stability of structured and unstructured data.

Locate defects, and disable blockers. This is where performance testing comes in. It quickly processes huge data volumes and estimates the speed, scalability and stability of structured and unstructured data.

Broaden your toolkit. Big data testing doesn’t rely on any particular testing tool; the gamut is wide and keeps expanding.

Go big or go home. Build a testing tool that lets you populate your installation with large amounts of data — any quick import or replication process you’re already using will do in this case.

This article originally appeared on To read the full article and see the images, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.


The Nastel i2M Platform provides:


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