Big Data & Data Engineering

Big Data

Big data the term that describes the large volume of data – both structured and unstructured – that inundates a business on a day – to – day basis. The growth of data is being measured in the same scale as population census, study of celestial bodies and behavioral science. They are huge in size (Volume), abstract and include numerous factors (Variety and Complexity), and are growing instantaneously (Velocity) as we speak.

Big Data

Big data can be analysed for insights that lead to better decisions and strategic business moves. The challenge more than managing data is how to put a noose around the new data that are being generated from new sources which include social media, web logs, location sensors, internet of things, video and music streams, sensors, and countless surveillance videos offering security and safety for global citizens. Brunet Info Solutions helps you to manage this sheer scale of data with Big Data Analytics. Our solutions are powered by the right mix of Big Data and Business Intelligence components allowing the enterprise to leverage the maximum out of a particular use case.

Big Data Analytics categorized into three stages

  • Data Integration – Acquire & Organize
  • Information Delivery – Present the data.
  • Data Analysis – Analyze and derive insights

Data Engineering

Brunet Info Solutions Data Engineering practice has a rich blend of industry-specific and function based innovative solutions that helps to deliver an insight-driven output to the customers.

As our customers come from various industries like Manufacturing, Supply chain, Oil & Gas, Telecom, Life Sciences, Consumer products and services verticals, we have extensive industry expertise which help to deliver solutions meeting customer specific business needs.

Our Data and Analytics services include Data Advisory & Strategy workshops, end-to-end implementations, managed services, technology assessments/audits and thought leadership. Our expertise span across data ingestion, pipelines, curation, cleansing, feature engineering, storage /warehousing, mining, visualization & reporting.

Data Engineering


  • Re-architecting
  • Migration to new software delivery model (such as Software as Service (SaaS), Platform as Service (Paas), and Delivery over cloud)
  • Technology and user interface upgrade
  • Porting and data migration
  • Legacy systems and application re-engineering
  • Enterprise application integration
  • Code re-structuring
  • Re-documentation
  • Platform transitions
  • Language conversion