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:
At Ennovision, we found 6 impeding factors to the slow adoption and realization of Big Data and Analytics
Here’s our approach to address them
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
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.
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.
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.