Data Engineering practice that serves enterprises with everything that needs to fuel their data & analytics needs

Today we are on an “information superhighway”. The sheer volumes of information exploited by technology has given rise to bundles of complexities. These increasing complexities have significant ramifications on how businesses manage and maintain data integrity. As more companies rely on Data Science to increase the velocity and veracity of their business decisions, clean, available, reliable data becomes crucial.

We help clients to derive insights from enterprise knowledge through best-in-class Data Engineering practices. These insights enable more informed and timely decision-making with the best possible Total Cost of Ownership (TCO).

The architecture of a data-driven enterprise must support reliable, scalable, and on-demand access to the entire corpus of enterprise data. Our Data Engineering Services help our clients get answers from their Big Data by deriving value within an agile and trusted data fabric. We work with businesses to design and implement architectures that make the entire corpus of enterprise data accessible.

Data Engineering helps you uncover valuable insights from all data and turn them into actions. Collecting data is NOT enough

One of the critical requirements for reliable Data Analytics and Data Science is clean, reliable data that has been transformed to meet the needs of data analysts and scientists. The Data Engineering layer provides the tools and environments that make the cleaning, matching, and transformations possible. This layer can also provide business-specific data rules to be applied to the data sets created for analysis.

With access to the entire corpus of enterprise data, it becomes possible to acquire and correlate insights across business functions and lines of business. This opens the door for analytics on a true enterprise-scale. Data silos are reduced and data is leveraged seamlessly without the restrictions of any artificial boundaries. As a result, insights are richer, and enterprise decision-makers are empowered accordingly. Our Data Engineering experts collaborate with clients to ensure that the data used for these decisions is reliable and current.

Our Data Engineering offerings

Enterprise Data Bus - Logo

Enterprise Data Bus (EDB), a modern data hub that creates visibility to all the data, centralizes control and provides a toolset to develop diverse views for multiple businesses and IT purposes. It is a modernized architecture, a framework, and an orchestration of software tools, workflows, and infrastructure that allows a data-driven enterprise to capture, store, analyze, and act on the corpus of enterprise data. Read More

Smart ingestion - Logo

We help clients prioritize their enterprise data sources and provide valuable context to data to drive insights on the business processes. This AI-driven toolset allows for the ingestion of diverse data sets into the data process flow, reducing the time and resources required to gather and input source data. Read More

Realizing more value from your data by Architecting Enterprise Intelligence with Data Engineering

We provide end-to-end processes to help you achieve your long-term Business Intelligence and Analytics goals. We use proven commercial and open-source technologies to instantiate the enterprise’s data environment. We work aggressively with clients to minimize support and life cycle costs.

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Data Engineering stack
  • Ontologies
  • Data


We help enterprises drive the real purpose of data engineering and make it even more relevant for decision-making. With Ontologies, we equip deep learning algorithms with contextual knowledge that makes it possible to derive truly actionable insights. Armed with domain-specific knowledge, your enterprise now is more intelligent than you think.


Data-driven enterprises require reliable, scalable access to the complete corpus of their enterprise data. We help our clients construct enterprise data environments that make their entire corpus of enterprise data available on demand. This enables the production of high-quality, reliable predictive models and is foundational for impactful machine learning and artificial intelligence.