Bangalore : +91 9480115687, +91 9945858107 Guragaon : +91 9953598108 Dubai : +971 (551) 263131 Houston : +1 (469) 8107653

Big Data, Cloud, eCommerce, QA Services


An online training platform, from DataMatrix Systems Bangalore on latest software technologies that offers exclusive training on domain specific areas, which are adopted by IT giants and MNC’s across the globe to accelerate their businesses. We provide online training, to students and professionals as to enhance their technology skills to meet the demands of job market across the globe , on the following areas like –

Collective Technology Platforms:

  • Big data processing using Hadoop and its eco system
  • Mobility
  • E-Commerce using Oracle Web Commerce
  • Cloud Computing

Software Testing:

  • Functional Testing
  • Automation
  • Performance Testing
  • UX Testing Tools for load/Stress Testing

Besides, we also provide software development, Consulting and Solutions-

  • E-Commerce development with multi-channel and omni-channel approach
  • Mobility on Android, iOS, Windows and Blackberry
  • Database servers

Our experienced professionals, from different parts of the world, offer training through this online platform. Students and professionals from any parts of the globe can access these training programmes, without going to classroom and prepare themselves for the vast emerging IT market for the jobs and equip themselves for entrepreneurial venture.

Case Studies


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

Read More 22

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 […]

Read More 0

Multichannel E-Commerce applications on top of Oracle Web Commerce(ATG)

December 9, 2016

We have published a paper in “ADBU Journal of Engineering Technology (AJET), an International online Journal.” ISSN:2348-7305 on ISO 8583 Messaging System. Paper title:- Usage of ISO 8583 Messaging System […]

Read More 16

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.