Databricks

Lakehouse builds on Unity Catalog and Delta Lake, Workflows orchestration, Delta Live Tables, and Databricks SQL for downstream analytics. Deployed into your own Azure or AWS UK.

Unity Catalog Delta Lake Workflows Databricks SQL

Microsoft Fabric

OneLake and workspace design, Data Factory pipelines, Lakehouse and Warehouse items, Real-Time Intelligence, and semantic models feeding Power BI — with capacity sizing that.

OneLake Data Factory Warehouse Power BI
Platform engineering

Lakehouse platform build on Databricks and Microsoft Fabric

Most data platforms are not failing because the technology is wrong. They fail because the workspace layout, access model, and storage layers were decided ad hoc, and nobody can now change anything without breaking a downstream report.

Deliverables
Target architecture with bronze, silver, and gold layer definitions
Unity Catalog or OneLake structure with catalogs, schemas, and domains
Environment separation across dev, test, and production with CI/CD promotion
Databricks Microsoft Fabric Unity Catalog Delta Lake OneLake
Pipelines and migration

Data pipeline engineering and legacy migration

Plenty of UK organisations are running business-critical reporting on SSIS packages written a decade ago, on-premises SQL Server, or a first-generation Synapse workspace that has outgrown its design. The migration keeps getting deferred because nobody can say.

Deliverables
Source-to-target inventory with dependency and lineage mapping
Phased migration plan with rollback points and a decommission sequence
Rebuilt pipelines in Databricks Workflows, Fabric Data Factory, or dbt
Databricks Workflows Fabric Data Factory dbt Apache Spark Azure Data Factory
Modelling and analytics

Data modelling, warehousing, and the semantic layer

A lakehouse full of clean tables is not the same thing as an organisation that can answer its own questions. The gap is usually the model — no agreed grain, no shared definitions, and three different versions of revenue depending on which report you open.

Deliverables
Dimensional models with documented grain, keys, and slowly changing dimensions
Semantic model in Power BI or Databricks SQL with certified measures
A business glossary mapping each metric to its owner and definition
Power BI Databricks SQL dbt Fabric Warehouse DAX
Governance and quality

Data governance, lineage, and data quality engineering

This is where our data work and our assurance work meet. We came to data engineering from testing, and we treat a pipeline the same way we treat a model — it is not finished until there is evidence it behaves correctly, and a suite that keeps proving it.

Deliverables
Catalog, classification, and sensitivity labelling for regulated data
End-to-end lineage from source system through to published report
Data quality rules as code, with freshness, volume, and distribution checks
Unity Catalog Microsoft Purview Great Expectations dbt tests Soda
Streaming and real-time

Streaming pipelines and real-time analytics

Not everything needs to be real time, and we will say so when a nightly batch is the right answer. But fraud screening, operational dashboards, telemetry, and event-driven workflows do need it — and they need it to keep working when a source system goes quiet.

Deliverables
Streaming ingest with exactly-once or at-least-once semantics as appropriate
Change data capture from operational sources into the lakehouse
Watermarking, late-arrival handling, and replay from a known offset
Structured Streaming Fabric Real-Time Intelligence Event Hubs Kafka Delta Live Tables
Cost and performance

Platform cost control and performance tuning

Databricks and Fabric both make it easy to spend a great deal of money without noticing. Oversized clusters that never scale down, capacity units bought for a peak that happens twice a month, and queries scanning far more data than they need are the usual.

Deliverables
Cost attribution by workload, team, and business domain
Cluster policy and autoscaling review, or Fabric capacity SKU right-sizing
Query and job tuning — partitioning, file sizing, caching, Photon adoption
Databricks Fabric Capacity Photon Delta Optimize Azure Cost Management

Not sure where your platform stands?

Tell us what you are running. We will review your architecture, pipelines, and spend, and come back within two working days with the three things worth fixing first — at no cost, under NDA.