Big Data processing engines, Storage, Ingestion, Securities, POC


An online learning and knowledge sharing platform on Big Data processing, Multi-Channel E-Commerce platform, Digital Payment Gateways and many more. Right place to learn and develop skills which are adopted by IT giants and MNC’s across the globe to accelerate their businesses. With our platform, you can evaluate the technical abilities of your teams, align learning to key business objectives and close skills gaps in critical areas like large volume of data security in a distributed environment, lightning fast data processing engines, cluster management etc. Process and analyze the exponential growth of digital data is the only option for the organizations to gain momentum in terms of growth. It’s not just about learning technology, development of proof of concept (POC) matters to evaluate technical issues. This platform helps you to move forward with the right approach, technology and the right skills. –

Technology Platforms for Big Data processing and analyzing:

  • Hadoop
  • Spark
  • Apache Flink
  • Data security
  • Kafka

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Our experienced professionals, from different parts of the world, offering videos, case studies, POCs, training through this online platform. Students and professionals from any parts of the globe can access those without going to the classroom and prepare themselves for the vast emerging IT market for the jobs. You are welcome to share your technical expertise through this platform in form of videos, study materials, case studies etc so that this online platform can be knowledge provider for the underprivileged community.

Case Studies


Performance Testing as Services (PTAS)

March 10, 2017

Software performance testing is one of the most critical and integral parts of overall quality control procedure of  software product/ project before release to the end user or market commercially. […]

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Effective Image Analysis on Twitter Streaming using Hadoop Eco System on Amazon Web

December 9, 2016

We have published a research paper on Hadoop and Ecosystem using real-time case study, in “International Journal of Advanced Research in Computer Science and Software Engineering” ISSN:2277 128X

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Proof of concept to analyse huge application log files using Hadoop cluster on IBM Cloud Platform

January 17, 2017

Analysing the application log files generated on production environment are very challenging. Data in the log files are in unstructured format and hence to leverage the query functionality, they can’t […]

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Hadoop - The Answer


The giant organizations across the globe are using legacy mainframe systems due to it's scalability, security and reliability of machine's processing capacity subjected to heavy and large workloads. Of course, these infrastructures desire huge hardware, software and processing capacity. As the technology advancing very rapidly, scarcity of mainframe technicians, developers etc are increasing and it has become a major challenge for those organizations to continue their operations. The maintenance/replacement of these hardware are also another threat due to low production of various parts by different vendors. Besides, performing analytics on mainframes systems is extremely inconvenient and comparing with the latest visualization tools, Graphical User Interfaces (GUIs) are not adequately supported by mainframes systems. Henceforth, many organizations have decided to migrate a portion of or the entire business applications involving batch processing running on mainframe systems to present-day platforms.

With the arrival of Big Data technologies into today's technology market, the mainframes' maintenance and processing expenses can be reduced by integrating a Hadoop layer or completely off-loading batch processing to Hadoop. Because Hadoop is an open source framework which is cost effective, scalable, and fault tolerant and can be deployed to clusters consisting of commodity hardware.

Offloading Mainframe Applications to Hadoop is now an achievable option because of its flexibility in upgrading the applications, improved short-term return on investment (ROI), cost effective data archival and the availability of historical data for querying. Huge volumes of structured and unstructured data plus historical data can be leveraged for analytics instead of restricting it to limited volumes of data in a bid to contain costs. This helps improve the quality of analytics and offers better insights on a variety of parameters to create value. Download Our Free E-Books!!