Big Data testing-The challenge and the opportunity
The possibility of 'unknown' scenarios in Big Data testing is gigantic when compared to testing techniques for conventional applications. The scope and range of the data harness in Big Data applications.
The inherent production of digital data across the economies and institutions is seen as an enormous source of information, which can help build a reliable knowledge base for critical decisions. As the IT enables global economy moves ahead, enterprises look at new ways of utilizing existing and growing data. At such moments, the Big Data perspective bridges the current and emerging trends.
Big data has purpose, little data has hope
While current trends suggest Big Data driven business as an avenue that requires substantial investments, the future will see a growth of Big Data apps by ISVs and Small and Medium Enterprise segment as well. Moreover, as business grows, enterprises need to accommodate and manage the increasing volume, variety and velocity of the data that flows into the IT systems.
The conventional columnar designs and horizontal databases demand continuous expansion to store and retrieve this data. The sheer volume in itself weighs on the cloud enabled schemas and sharding techniques, forcing enterprises to look for new ways to accept, model and discard the data. Findings of an MIT research project by Andrew McAfee and Erik Brynjolfsson indicate that companies which inject big data and analytics into their operations show productivity rates and profitability that are 5 to 6 percent higher than those of their peers.
The possibility of 'unknown' scenarios in Big Data testing is gigantic when compared to testing techniques for conventional applications. The scope and range of the data harness in Big Data applications will demand new benchmarks of Software Quality Assurance.
To accommodate Big Data test requirements, processes and infrastructure will be redesigned to achieve new levels of scalability, reusability and compatibility to ensure comprehensive, continuous and context driven test capabilities. To handle the volume and ensure live data integration, Big Data testing needs to empower developers and enterprises with freedom to experiment and innovate.
One data layer
From a Big Data perspective, enterprises will seek validation of application design, data security, source verification and compliance with industry standards. The parameters of performance, speed, security and load will add magnitude and precision to sculpt and reorganize data volumes into blocks that match the emerging requirements.
Over time, the database and storage layers will merge into a single data layer with options of retrieval and transmission exported out of the layer.
Business leaders now look at data maps to estimate and draft plans for emerging scenarios. The transformation of data into comprehensive reports in real time will add value to business decisions and enrich operations with higher levels of speed and accuracy. The test capabilities will acquire ability to de-complicate data sources/types/structures and channel them along specified contexts to align with objectives.
In a story titled 'The Top 7 Things Obama Taught Us About the Future of Business', the Forbes reported that the Obama campaign used a test tool called 'Optimizely' to improve efficiency. Dan Siroker, Co-founder of Optimizely, was quoted as saying "we ran over 240 A/B tests to try different messaging, calls to action, and in attempt to raise more money. Because of our efforts, we increased effectiveness 49 percent."
Why Big Data is a good opportunity for Software Testers?
Consider this. A joint report by NASSCOM and CRISIL Global Research & Analytics suggests that by 2015, Big Data is expected to become a USD 25 billion industry, growing at a CAGR of 45 per cent. Managing data growth is the number two priority for IT organizations over the next 12-18 months. In order to sustain growth, enterprises will adopt next generation data integration platforms and techniques fueling the demand for Quality Assurance mechanisms around the new data perspectives.
Be a smart tester and ride the next wave of IT on Big Data
Testers can formulate service models through operational exposure to data acquisition techniques on Hadoop and related platforms. Test approaches can be developed by studying the deployment strategies of Mahout, Java, Python, Pig, Hive etc. Contextualization of data from diverse sources to streamlined outputs helps testers understand the channels of business logic in the data science.
Big Data is an emerging discipline which will leave a profound impact on the global economy. The ability to explore the power of Big Data testing is like being in a hotspot that will see action in terms of innovations that match emerging test requirements.
Tag Words: independent testing, software, service, testing, cigniti, quality assurance, software testing, big data, big datatesting
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