Lakehouse Development

What is a Lakehouse?

The Lakehouse is the next generation of data infrastructure: an architecture that combines a data lake's ability to store any kind of information at low cost with the capabilities of a classic data warehouse — transactions, schema management, query performance and reliability. Instead of maintaining two separate platforms and keeping them in sync, your organisation gets a single platform that serves BI reports, analysts, data scientists and AI agents.

When a Lakehouse, and when a data warehouse?

• Data warehouse (DWH) — when the main need is reports and dashboards on structured data from your operational systems.
• Lakehouse — when the organisation also works with unstructured data (files, logs, documents, sensors), needs real-time data, or plans AI and machine-learning use cases.
• Combined — in practice, most projects build a Lakehouse that also feeds a semantic warehouse layer for BI — the best of both worlds.

The technologies we work with

• Microsoft Fabric and OneLake — Microsoft's unified data platform, fully integrated with Power BI.
• Databricks — the Apache Spark-based Lakehouse platform for large-scale data engineering and AI.
• Delta Lake — the open storage layer that gives a data lake the reliability of a data warehouse.

What does a Lakehouse project look like with us?

1. Scoping — mapping data sources, business needs and usage scenarios (BI, analytics, AI).
2. Architecture — designing the Medallion layers, the permissions model and the loading strategy.
3. Development — building data pipelines, transformations and data-quality monitoring.
4. Semantic layer — connecting to Power BI with a fast, secure data model for end users.
5. Rollout and support — training your teams, ongoing monitoring and gradual expansion to new scenarios.

Why now?

Every serious AI initiative starts with well-organised data infrastructure. A Lakehouse is the foundation that lets your organisation not only look at the past through reports, but run models, automations and intelligent agents on fresh, reliable data — without rebuilding the infrastructure at every step.