
Designing a thin and scalable data engineering layer suitable for the cloud with pay-per-use to minimize costs.
Handling data integration and transformation from multiple ERPs to ensure seamless data flow and accurate analytics.
Result:
The platform provided efficient data integration, transformation, and storage, ensuring scalability and cost-effectiveness while supporting future growth.
Detailed Solution:
Impact:
The implementation led to a scalable, efficient, and cost-effective data engineering platform that streamlined data integration and transformation, enhancing the client's ability to make data-driven decisions.
Future-Ready Data Architecture:
The platform is built with extensibility at its core, supporting seamless integration of emerging Azure services and third-party data sources. The modular design allows for easy adoption of advanced analytics capabilities, machine learning models, and real-time streaming as business needs evolve, without requiring architectural overhauls.
What Our Client Says
"Entrans delivered a data platform that exceeded our expectations in both performance and cost efficiency. The Azure-based solution they architected has become the backbone of our analytics operations, processing terabytes of data daily while keeping costs predictable. Their expertise in optimizing Spark workflows and designing the curated data lake approach has positioned us perfectly for our next phase of growth and AI initiatives." - Chief Data Officer, Enterprise Technology Company
The platform ensured seamless data flow and accurate analytics by handling data integration and transformation from multiple ERPs.

The platform ensured seamless data flow and accurate analytics by handling data integration and transformation from multiple ERPs.

The architecture allowed for the addition of any micro-strategy over the data architecture, supporting future growth and scalability.


