Data Engineering & Pipeline Development
Pipelines that run reliably, fail loudly, and can be explained to whoever asks where a number came from.
Read moreData & AI
Data Warehousing & Analytics
A warehouse and model that answer the questions the business actually asks, at a cost you can predict.
Overview
Warehouse projects go wrong when they start from the data available rather than the decisions to be supported. The result is a technically impressive model that nobody queries, alongside the same spreadsheets circulating by email.
We start from the questions — the ones people currently answer by hand — and work backwards to the model, then design for cost from the outset, because partitioning and clustering decisions are considerably cheaper to make early.
What you get
Modelled around decisions
Dimensional models built from the questions the business asks, not from whatever the source systems happen to expose.
One agreed definition
Metrics defined once and reused, so finance and operations stop arriving at different revenue figures.
Query cost controlled
Partitioning, clustering and materialisation designed early, when the decisions are cheap to make.
Self-service that works
A semantic layer and documentation so analysts answer their own questions without writing raw SQL.
How we work
- 01
Discover
Decisions, existing reports and the manual workarounds people have built are catalogued.
- 02
Model
Dimensional design agreed with the business, with metric definitions written down and signed off.
- 03
Build
Warehouse, transformations and tests implemented, with documentation generated from the code.
- 04
Adopt
Dashboards delivered and analysts trained, with the old reports formally retired.
Common questions
Do we need a warehouse, or is a database enough?
If reporting queries are slowing your production database, or analysis spans several systems, a warehouse pays for itself. Below that, a well-indexed read replica is often sufficient.
Which platform?
BigQuery for simplicity and serverless pricing, Snowflake for multi-cloud portability, Redshift or Synapse where you are committed to one provider. All are capable; fit matters more.
How do we avoid runaway query costs?
Partitioning, clustering, materialised views for common queries, and per-user or per-team quotas so one unbounded query does not consume a month of budget.
Often paired with
Google Cloud Platform (GCP) Solutions
Google Cloud design and delivery, particularly where data, analytics and machine learning lead the decision.
Read moreMLOps & AI Infrastructure Consulting
Infrastructure, pipelines and evaluation for AI systems — including telling you when you do not need one.
Read moreReady to talk about data warehousing & analytics?
We will tell you what we would do, roughly what it costs, and whether it is worth doing yet.