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Big Data and Analytics

The characteristics of an optimized Data model are : Performance, Scalability and High availability.

Almost every company worth its salt now talks about Big Data in every breath they take, the early adopters have been able to ride the wave and work their way around the challenges to identify new opportunities for growth.
For most legacy companies, including traditional retailers and High street banks, the adoption and therefore the success of Big Data Analytics has been severely restricted. With three-fourths of them achieving even less than 1 % of revenue increment or cost improvement, having implemented Analytics in some shape and form, within the business. The start-ups however, are flourishing with the early adoption and Big Data Analytics and whilst making it a foundation of their businesses.

The 4 key elements comprising BIG DATA are the 4 V’s:

  • Volume: The amount of data being created is vast compared to traditional data sources
  • Variety: Data comes from different sources and is being created by machines as well as people
  • Velocity: Data is being generated extremely fast — a process that never stops, even while we sleep
  • Veracity: Big data is sourced from many different places, and as a result you need to test the veracity/quality of the data

At Ennovision, we found 6 impeding factors to the slow adoption and realization of Big Data and Analytics

  • The businesses failure to appreciate the potential of big data analytics. Considering there aren’t enough immediate financial returns to justify additional investments. 
  • Ability to tie down the often scattered, structured and unstructured data coming from multiple channels from within and outside the organization.
  • Shortage of skills and resources.
  • The lack of understanding and confidence in the analytics and there-fore the hesitation to employ it.
  • The rigidity of the organizational structure and processes impeding advancements in analytics and automation.
  • Here’s our approach to address them

    Help design a short to medium term strategy around the fast-evolving landscape of data analytics.
    Outcome Based Approach
    Assess, pick up and run POCs on business cases that could bring relatively quicker returns on investment.
    Flexible and Scalable
    Provide the flexibility of having experienced teams and latest tools on demand with minimum to no upfront investment.

    Data Ingestion:

    Fetching data from multiple sources is an important aspect of Big Data analysis. OUR team has extensive experience on bringing data to Hadoop from multiple sources like
    — RDBMS : Teradata, Oracle, MySQL, etc
    — Flat Files : CSV, TSV, any semi structured files
    — Raw data (through Flume & Kafka) : Web logs, Application logs 

    Data Mart [Marketplace for Data]:

    The integration platform that enables applications to mesh and connect a variety of cloud services. A key building block in the platform is a data management
    system [Data Lake] that receives events for various interactions that happen on their integration platform. It is needed to acquire, process and analyze these data
    at near real-time speeds with extremely high reliability and statistical precision.

    Performance Benchmarking:

    AN AWS Cloud based data lake solution, scalable and high available system to hold and serve data with an ability to support the report query while maintaining an
    acceptable performance level.

    Internet Of Things [IOT]:

    The Micro-services based Internet of things software platform [IOTSP] framework that will enable to reduce the daily operational cost, manual framework complexities so that the engineering team can focus on building new micro-services and process improvement. The complete framework would use a modern cluster management and distributed system for running micro-services applications in distributed mode. All the jobs would be configured at central location i.e. GIT, which enables easy build and change management. Micro-services project packaging as Docker images for faster deployments. Finally, the framework would be addressing the Continuous Integration and Continuous deployment [CI / CD] pipeline to allow engineering teams including QA to perform staging and production deployments.

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