Lakehouse on GCP

Data Warehouse

A governed BigQuery foundation that turns operational data into reusable business models inside your GCP project.

Outcome

A governed BigQuery lakehouse with reusable models, controlled access, and one production path from source systems to consumers.

Typical timeline

4–8 weeks for the first production scope, depending on source access, data quality, and model breadth.

Best for

SMEs whose reporting, automation, or analysis is constrained by disconnected sources and competing definitions.

What you actually get

You get a governed analytical layer between operational systems and every report, alert, or application that consumes their data.

Core entities and metrics are modeled once, with source behavior and business decisions made explicit.

Pipelines, access, metadata, quality checks, and infrastructure are versioned so the platform can change without returning to spreadsheet archaeology.

  • Reliable ingestion from operational databases, APIs, files, and external sources.
  • Reusable models for customers, products, orders, invoices, and other agreed domains.
  • Governed metric definitions for reporting, alerts, and downstream systems.
  • Access control, lineage, quality checks, monitoring, and cost guardrails.
  • Serving views shaped for BI tools, Google Sheets, APIs, or internal applications.

You're here when

  • Teams calculate the same business concept independently and spend meetings reconciling it.
  • Recurring reporting starts with manual exports, cleanup, and undocumented joins.
  • Operational systems cannot safely carry the analytical workloads the business now needs.
  • Every new dashboard or automation project has to rediscover the same source logic.

How the platform works

Source data is preserved, business meaning is modeled explicitly, and consumers read governed outputs instead of rebuilding source logic.

  1. 01

    Ingest operational and reference data through recoverable, observable pipelines.

  2. 02

    Preserve source identity, timestamps, deletion behavior, and extraction metadata for replay and diagnosis.

  3. 03

    Model shared entities, transactions, relationships, and history at declared grains.

  4. 04

    Publish governed metrics and decision-specific marts for reporting, alerts, and applications.

  5. 05

    Operate the platform through versioned infrastructure, tests, documentation, access controls, and monitoring.

What the project looks like

First production scope in 4–8 weeks, depending on the number of sources, model boundaries, and source inconsistency.

Phase 1: Decisions and contracts

Start from the reports, workflows, and decisions that need trustworthy data, then define the source and model boundaries.

  • Identify the first consumers and the business concepts they need.
  • Map source authority, grain, identity, history, access, and freshness requirements.

Phase 2: Build the foundation

Implement ingestion, governed models, access, and the first serving outputs inside your GCP project.

  • Set up BigQuery, pipelines, Dataform models, IAM, metadata, and quality checks.
  • Connect the first reports or workflows to the governed outputs.

Phase 3: Harden and hand over

Prove recovery, observability, cost boundaries, and ownership before expanding the platform.

  • Add monitoring, runbooks, reconciliation, and controlled backfill paths.
  • Document the platform and hand it over with a clear route for new sources and models.

What becomes possible after this

A stable data foundation lets reporting, alerts, and internal systems reuse business meaning instead of rebuilding it for every project.

  • Operational alerts can act on governed metrics and stable entity identity.
  • Dashboards can add markets, products, and teams without forking the underlying definitions.
  • APIs and internal tools can consume deliberate data products rather than exports and source-specific joins.
Alerts that use the same definitions Reporting built on the governed model Long-term support that keeps the platform stable

This is overkill if

One reliable spreadsheet still answers the business need at acceptable cost.

The first requirement is a bounded report rather than a reusable data foundation.

Nobody can own source access, business definitions, or the platform after handover.

Start from the decision the data must support

Bring the current reports, source systems, and recurring reconciliation work. We’ll identify whether you need a warehouse, a smaller reporting fix, or no new platform yet.

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