Data · Service 05

Data Engineering.

Lakehouse builds, pipelines, quality and migrations — a trusted, governed data foundation that analysts, scientists and AI products can rely on.

40+
Lakehouses delivered
99.8%
Avg. pipeline SLA
−62%
Avg. batch runtime post-migration
100+
Sources integrated
Overview

The foundation everything else depends on.

Every dashboard, model and AI agent is only as good as the data underneath. We build modern lakehouses with governance, quality and lineage baked in — not bolted on.

We're partner-certified across Databricks, Snowflake, Microsoft Fabric and the major cloud warehouses. Recommendations are based on your stack, skills and commercials — not our margin.

What we build.

01

Lakehouse & warehouse

Medallion architecture (bronze / silver / gold), governance, access controls, cost governance from day one.

02

Batch & streaming pipelines

Idempotent, observable, tested. Kafka / Kinesis / Event Hubs where real-time matters; orchestrated batch where it doesn't.

03

Data quality & lineage

Expectations-as-code, freshness SLAs, column-level lineage, data-product ownership. Not a manual audit.

04

Migration & modernisation

From legacy DW (Teradata, Netezza, Oracle DW, on-prem SQL) to modern lakehouse, with parallel-run and cut-over playbooks.

05

Integration

ERP / CRM / HRIS / core systems into the lakehouse. Fivetran, Airbyte, custom CDC — whichever fits.

06

Operate & optimise

FinOps on your lakehouse, cluster right-sizing, partition/tuning reviews, ongoing SRE support.

How we work

Working platform in weeks, not quarters.

1
Weeks 1–2

Discover

Source audit, use-case alignment, target architecture sign-off.

2
Weeks 3–6

Foundation

Lakehouse stood up, first three domains onboarded, governance scaffolding.

3
Weeks 7–16

Scale

Domain-by-domain onboarding, quality gates, analyst enablement.

4
Ongoing

Operate

SRE, FinOps, platform evolution, new-domain playbook.

Deliverables

  • Production lakehouseGovernance, lineage, quality, access — in your cloud.
  • Pipeline libraryIngestion, transform, publish — all as code, all tested.
  • Data-product catalogueOwned, documented, SLA'd datasets consumers can trust.
  • Cost & performance baselineFinOps dashboard, optimisation backlog.
  • Operator runbookHow your team runs and extends the platform.
  • Migration retrospectiveFor migration engagements — lessons, risks, residual work.
Technology

Tools & frameworks we use.

Databricks
Snowflake
Microsoft Fabric
dbt
Airflow
Dagster
Fivetran
Airbyte
Kafka
Confluent
Delta Lake
Iceberg
Great Expectations
Soda
In production

A real engagement.

Case study

Housing group — legacy DW → Databricks lakehouse.

Three-year roadmap delivered in 14 months. 48 source systems onto a Databricks lakehouse; overnight batch window cut from 11h to 3.5h; licence cost down 41%.

Read full case study
48
Sources onboarded
11h → 3.5h
Batch window
−41%
Annual licence cost
14 mo
Delivered in
FAQ

Common questions.

Databricks or Snowflake?+
Depends on your workloads, skills and commercials. We run a structured evaluation with you. Both are great — the wrong choice costs you nothing, the wrong configuration of either costs a lot.
Do we need to migrate everything at once?+
No — and usually you shouldn't. We use a domain-by-domain strangler pattern with parallel-run, so the old system keeps working while the new one takes over.
Can you work with our existing team?+
Yes. We often lead the foundation then pair with your engineers through domain rollout, so capability stays when we leave.
What about data quality?+
Built in from day one — expectations-as-code, freshness SLAs, alerts. Not a cleanup project six months later.
Ready when you are

Put your data to work.

Book a free 30-minute consultation with a senior Databuzz consultant.