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Data Engineering and Modernization Services

We build the pipelines and platforms that collect data from your applications, clean it and store it in a modern warehouse or lakehouse, so analysts, dashboards and AI models all work from the same reliable source.

Plan Your Data Platform

Network cables connected to a server switch

The scope

What Is Included

  • Data Pipelines

    Batch and real-time pipelines that extract, transform and load data from every source.

  • Warehouses and Lakehouses

    Scalable storage on BigQuery, Snowflake, Databricks, Microsoft Fabric or Amazon Redshift.

  • Database Modernization

    Legacy databases moved to modern managed platforms, with validated data.

  • Data Quality

    Automated tests and alerts for missing, duplicate or inconsistent data.

  • Data Cataloguing

    Definitions and lineage, so everyone knows what each metric means and where it comes from.

  • Performance and Cost

    Partitioning, scheduling and storage choices that keep queries fast and costs predictable.

Signs It Is Time

  • Reports from different teams show different numbers.
  • Data arrives too late to act on.
  • Legacy databases are slow and expensive to maintain.
  • Analysts spend more time preparing data than analysing it.

Tools We Use

  • Google BigQuery
  • Snowflake
  • Databricks
  • Microsoft Fabric
  • dbt
  • Apache Airflow

Questions About Data Engineering & Modernization

What is the difference between a data warehouse and a data lake?

A warehouse stores structured, modelled data for reporting. A lake stores raw data of any type at low cost. A lakehouse combines both, usually on open table formats such as Apache Iceberg, so one platform serves reporting, data science and AI.

Can you work with our existing databases?

Yes. We connect to the databases and applications you already run and modernize them in stages rather than replacing everything at once.

We run Azure Synapse. Do we need to move?

Not urgently. Synapse is still supported, but Microsoft is putting new investment into Microsoft Fabric. We plan a staged move when it suits your roadmap, starting with new workloads.

How do you keep data accurate?

Automated quality tests run on every load, failures raise alerts, and each metric has one agreed definition.

Explore Data & AI

Give Every Team the Same Trusted Numbers

List the systems your data lives in. We will design the pipelines and the platform that bring it together.

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