Early 2012, client management decided to build a single platform for printer data analysis, capable to handle huge data volume in open source technologies which ensures low cost and high performance

Build an application that will accept the granular level attributes from the printer feeds and store in a Hadoop based Data Repository

The layered architecture wasExtract – JBossTransformation – HadoopLoad – Vertica Advanced analytics were

planned in Informatica & Vertica ( Later offloaded to Hadoop )

Project Overview

Big data Implementation : Case Study

The client is an American multinational information technology corporation. It provides products, technologies, software, solutions and services to consumers, small- and medium-sized businesses (SMBs) and large enterprises.

Client Overview

Process huge volume of semi structured live data in the order of 2 TB / day.

Data from different sources. Complex transformations & algorithms. Variety of data association & aggregations. Rapidly growing data volumes. Cost Efficient Hardware Scalability. Integration and Quality Assurance different

sources (Jboss & Vertica) Systems capability to reprocess subset of data Variety of reports to be generated Multi-Layer Architecture Infrastructure readiness & Network

connectivity Issues

Driver/ Issues/Challenges

Created an architecture to address client’s single platforms in Big dataCreated a generic parser framework layer to convert un/semi structured , multi formatted data to a structured dataCreated a validation framework to validate the data as per the business requirementsCreated a transformation layer to execute complex business transformationsCreated data formatter layer to format data in to customer expected formatSuccessfully implemented Big data solution addressing the below business area’s Active variety of Ink Jet Printers Passive variety of Ink Jet Printers Laser Printer ( in progress ) Web Based Printer ( in progress ) Advanced transformations for all printer’s

TCS Solution

Single Platform for all printer types. Optimal hardware resource utilization Data Analytics with historical, daily data. Maintained an Invalid data logging

mechanism. Which help customer to identify data lose.

Fully automated data processing and alerting mechanism

Advanced analytics also offloaded to Hadoop because of performance satisfaction factor

New business transformations are offloading to Hadoop

.

Results and /Business Benefits