Overview
When sales, finance and marketing each pull their own export, meetings turn into debates about whose total is correct. We set up a managed warehouse, load your sources automatically and model them in layers, so each business definition is written once, versioned and protected by tests. If an upstream change quietly breaks a figure, a check fails before anyone quotes the wrong number. Your team gets a dependable base for reporting now and for machine learning projects later.
What you receive
- A managed warehouse database set up with clearly defined access roles
- Scheduled loading from as many as five sources, such as app, payments and ads
- Layered models moving raw data through staging and intermediate steps into business marts
- Tests for uniqueness, broken references and stale data on your critical tables
- An auto-built documentation site mapping how each model depends on others
- Daily scheduled runs that alert your team by email or chat on failure
- A handover explaining exactly how your team adds new models later
How it runs
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Step - 01
Discover
We list the business questions your current reports cannot answer with confidence.
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Step - 02
Design
Each source is mapped and the business marts are shaped around those questions.
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Step - 03
Build
Loading, transformation models and tests are written, then historical data is backfilled.
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Step - 04
Handover
A guided walkthrough, written docs and two weeks of follow-up support for questions.
Timeline: 25 working days, counted from the day we receive what we need from you.
Is it a fit?
A good match for
- Businesses where each report is built from a separate spreadsheet
- Analysts who lose most of their week merging exports by hand
- Companies heading into investor due diligence that need consistent numbers
What we need from you
- Read-only logins for each source system and connected platform
- One hour with the owner of each metric definition under debate
- A decision on hosting location and which budget pays for the warehouse
Why it helps
- Each metric has one agreed value, defined in code and checked by tests
- Problems in source data fail a test before a customer notices anything
- Analysts spend their time interpreting data instead of assembling it
Questions
Not always. For moderate data volumes a managed open-source database is often cheaper and simpler. We size the options with you before anything is provisioned.
It is very common. Cleaning happens in the staging layer, and anything that genuinely cannot be corrected is written up as a known limitation instead of being hidden.
No. This service builds the modelled data foundation only. Dashboards are delivered through our separate reporting service, which is designed to run on top of this warehouse.