Why the Lakehouse
One platform for data, analytics and AI
Most organisations run separate systems for data lakes, warehouses and machine learning. Data is copied between them, governance is fragmented and AI projects stall waiting for clean data. The Databricks Lakehouse brings these workloads onto one open platform with one governance layer.
What we do
We design Databricks platforms on AWS, migrate legacy data workloads, build production pipelines and put governance in place so your data is ready for analytics and AI.
Our engineers work in your workspaces and repositories from day one, so the platform is built the way your team will run it.
Where it fits
Databricks is the data foundation behind our Enterprise AI and Industry 4.0 work. RAG applications, AI agents and IIoT analytics all depend on clean, governed and well-modelled data.
We connect the Lakehouse to the rest of your estate through our Integration Engineering practice.
What We Deliver
Databricks services
01
Databricks on AWS setup
Workspace design, networking, identity, security controls and infrastructure as code for repeatable environments.
02
Lakehouse migration
Migration of Hadoop, legacy data warehouses and on-premise ETL to Databricks with validation at every step.
03
Delta Lake pipelines
Batch and streaming pipelines using a medallion (bronze, silver, gold) architecture with testing and monitoring built in.
04
Unity Catalog governance
Central access control, lineage, data quality and auditing across all data and AI assets.
05
ML and GenAI with Mosaic AI
Feature engineering, model training, model serving and RAG applications built directly on your governed data.
06
Cost and performance optimisation
Cluster policies, job tuning, storage optimisation and FinOps reporting to keep Databricks spend under control.
How We Deliver
From assessment to a platform your team owns
1
Assess
We review current data platforms, workloads, costs and skills and define the target Lakehouse architecture.
2
Design
Workspaces, security, catalog structure and pipeline standards are designed and agreed with your team.
3
Migrate and build
Workloads are migrated in waves and new pipelines are built, with automated validation against source systems.
4
Govern
Unity Catalog policies, lineage and data quality checks are applied so data is trusted and compliant.
5
Optimise and transfer
Performance and cost are tuned and your team is trained to operate and extend the platform.
Connected Practices
Where Databricks powers the rest
Databricks Readiness & Migration Assessment: a fixed-scope review of your current data estate with a target architecture and migration plan. Investment: As per the SOW Contract.
Build your Lakehouse with engineers in the room
Tell us about your data estate and goals. We will scope a readiness assessment or a first migration wave.