Data modernisation and migration to Databricks

A data platform ready for AI agents

An AI agent is only as good as the data it can read. Modernising the platform means making that data reliable, governed and safe to access, while keeping the bill under control.

What the module covers

Migration: SQL Server estates to Azure, Kubernetes and Databricks, tested before cutover, with a rollback plan.
Governance: catalogue, access rights, data lineage.
FinOps: DBU cost tracked and reduced, clusters sized to the workload.
Pipelines ready for generative AI: documented data your agents can use without exposing sensitive data.

Experience at high volume

18 years of experience, including the critical infrastructure of an estate of 400+ SQL Server instances (several petabytes) alongside Databricks, PostgreSQL and Elasticsearch, with an SRE approach and Infrastructure as Code.

For a SQL Server upgrade on its own, see SQL Server migration. To automate operations once you have moved, see production-grade AI agents.

Frequently asked questions

Should everything move to Databricks?

No. Transactional databases often stay on SQL Server or Azure SQL; Databricks takes analytics, pipelines and AI workloads. We decide together what moves, what stays and in which order.

How do you keep Databricks costs under control?

With FinOps from the design stage: cluster sizing, compute policies, DBU consumption tracked per team and per pipeline, and alerts before the bill drifts.

What does "pipelines ready for generative AI" mean?

Reliable, documented and governed data with clear access rights, which AI agents or models can use without exposing sensitive data.

Do you work with Kubernetes?

Yes, with an SRE approach and Infrastructure as Code: environments are described in code, reproducible and monitored.