Lakehouse on GCP

Data Warehouse

One source of truth, running as a BigQuery lakehouse inside your GCP project with a clean handoff to your team.

Outcome

One warehouse. One source of truth. One set of numbers the whole company uses.

Typical timeline

4–8 weeks to replace spreadsheet reporting with an organized, production-grade warehouse.

Best for

SMEs that outgrew spreadsheets and have teams debating whose numbers are 'right'.

What you actually get

You get a warehouse that behaves like infrastructure. Not a pet project. Not a folder full of exports.

Revenue, margin, churn, and inventory move into one model instead of six competing spreadsheets.

Every report pulls from the same definitions so you stop rewriting logic for the hundredth time.

  • Modeled domains for customers, products, orders, and invoices.
  • Defined metrics and entities instead of tribal knowledge and Slack guesses.
  • A metric layer that defines KPIs once and makes them reusable everywhere.
  • Built-in guardrails for access, lineage, and changes so definitions don’t drift over time.
  • Views shaped for the tools you already use: Looker Studio, Sheets, or custom apps.

You're here when

  • Finance, sales, and operations each defend a different spreadsheet as the 'real' numbers.
  • Month-end becomes a cleanup ritual because nobody trusts last month's definitions.
  • Every dashboard request becomes another one-off SQL resurrection.
  • Your team spends more time reconciling contradictions than making decisions.

How it works under the hood

The warehouse follows a simple pattern: raw › modeled › metric. Data lands once, gets cleaned once, and every consumer reads from the same surfaces instead of inventing their own.

  1. 01

    Ingest from ERP, CRM, e-shop, and marketing systems into raw BigQuery tables without manual detours.

  2. 02

    Model domains in a silver layer: customers, products, orders, invoices.

  3. 03

    Define a gold metric layer for revenue, margin, retention, and inventory that doesn’t drift every month.

  4. 04

    Expose views built for BI and internal tools instead of exploratory SQL experiments.

  5. 05

    Automate jobs and checks so the pipeline execution is deterministic, not accidental.

What the project looks like

First version in 4–8 weeks, depending on how many systems you connect and how inconsistent they are.

Phase 1 – Mapping and contracts

We anchor scope in the reports that actually run the business and lock the boundaries before touching data.

  • Identify the reports that drive decisions, not the ones nobody opens.
  • Map entities, metrics, and contracts so every system knows its role.

Phase 2 – Warehouse build

Ingestion, modeling, and the metric layer come online inside your GCP project — predictable, reviewable, and owned.

  • Set up ingestion, models, IAM, and the metric layer inside your GCP project.
  • Connect the first reports to your BI tools using the new definitions.

Phase 3 – Hardening and handover

The warehouse is stabilized, documented, and handed over so it behaves like infrastructure, not a fragile project.

  • Add tests, alerts, and monitoring so drift is caught early.
  • Document the structure and hand it over with a clean extension path.

What becomes possible after this

Once the warehouse is stable, it becomes a backbone you can build on — alerts, dashboards, and internal tools stop improvising and start using the same definitions.

  • Alerts that fire when revenue, margin, or inventory drift instead of when someone finally notices.
  • Dashboards stay stable when you add products, markets, or teams because the definitions don’t move.
  • Internal tools can hit simple APIs for clean numbers instead of scraping exports or guessing logic.
Alerts that fire off of the same definitions Reporting that sits on top of the model Long-term support that keeps it all stable

This is overkill if

A single spreadsheet still covers 90% of your reporting without slowing you down.

Your metrics change every month because nobody has agreed on what they mean.

You need a quick dashboard, not a foundational system.

Ready when you are

Bring the reporting mess, walk through the stack, and Anriku will tell you if a warehouse is actually worth doing.