Data engineering services design and build the pipelines, warehouses and lakehouses that collect data from your systems, clean and organise it, and deliver it reliably to reports, analytics and AI applications.
Analytics and AI fail without dependable data flowing in on time. Our data engineering services design and build scalable pipelines that collect, transform and deliver data from every source into a modern warehouse or lakehouse, in batch or in real time.
Build robust data pipelines and architectures to power analytics and AI. At Prominent AI Services LLP, we deliver data engineering services end to end, from discovery and data preparation to deployment, monitoring and support, so your teams get dependable results quickly and securely.
Build robust data pipelines and architectures to power analytics and AI.
Ideal for: Finance, Logistics, SaaS, E-commerce. We begin by mapping data sources and business questions, then design a scalable architecture, automate data quality checks and deliver analysis and dashboards in short iterations with business users involved throughout.
We build on Microsoft Fabric, Azure Data Factory and Synapse, Databricks, Snowflake, and AWS services such as Glue and Redshift. Transformations are written in SQL, dbt and Python/PySpark; streaming uses Kafka or Azure Event Hubs. Pipelines are version-controlled, tested and deployed automatically, with data-quality checks and alerting built in.
Choose a fixed-scope platform build, or hire data engineers and ETL developers to join your team. For connecting live systems and APIs, see data and API integration and real-time data monitoring.
Need a reliable data foundation? Tell us your sources and goals, and we will suggest an architecture and a phased plan. Start your AI journey.
Common questions about our Data Engineering services.
We build on Azure Data Factory, Synapse, Microsoft Fabric, AWS Glue, Redshift, Snowflake and Databricks, depending on your cloud strategy.
Yes. We implement streaming with Kafka, Event Hubs and Kinesis for use cases such as fraud alerts, IoT telemetry and live dashboards.
Automated testing, data quality checks, alerting and orchestration with tools like Airflow keep pipelines observable and resilient.
Cost depends on the number of sources, data volumes and how fresh the data must be. We usually deliver in phases, starting with the highest-value sources, at a fixed price per phase.
A first version with a few key sources typically takes six to ten weeks. More sources are added in later phases.
A warehouse suits structured reporting data; a lakehouse also handles files, logs and data for machine learning. Many organisations now use a lakehouse such as Microsoft Fabric or Databricks. We recommend based on your data and skills.
Yes. We migrate SSIS packages, scripts and older ETL tools to modern cloud pipelines, running old and new side by side until the results match.
