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Data engineering

Google BigQuery

Google's serverless data warehouse: no clusters to manage, just SQL.

  • Serverless data warehouse
  • Typical project: 6–14 weeks
  • Google
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Overview

BigQuery runs SQL over huge datasets without you managing servers. You load or stream data in, query it, and pay either for what you scan or for reserved capacity.

It connects natively to Google Analytics, Google Ads and Looker, which makes it a favourite of marketing and digital teams, and it has built-in machine learning and Gemini features.

Names you'll meet

  • BigQuery
  • Looker
  • Dataflow
  • dbt
  • Gemini

What's inside

  1. 1

    Serverless SQL engine

    Scales automatically for every query.

  2. 2

    Streaming ingestion

    Real-time inserts for live dashboards.

  3. 3

    BigQuery ML

    Train simple models with plain SQL.

  4. 4

    Governance

    Dataplex catalogue and fine-grained access controls.

Is it right for you?

Best for

  • Organisations on Google Cloud
  • Digital, marketing and web analytics
  • Teams that want zero infrastructure

Think twice if…

  • Nobody will watch query costs; partition tables and cap spend
  • Your estate is centred on Azure or AWS

Typical projects

  1. Marketing data warehouse6–14 weeks
  2. Warehouse migration to BigQuery3–7 months
  3. Real-time dashboards4–10 weeks

Typical ranges, not quotes. Scope, data and team size move them most.

Roles you'll hire

  • Data engineer
  • Analytics engineer
  • Cloud architect
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Questions to ask a vendor

  1. 1On-demand or capacity pricing: which do you recommend for our usage?
  2. 2How will you partition and cluster our largest tables?
  3. 3How will you control who can see personal data?
  4. 4Have you integrated our marketing sources before?

Find BigQuery partners

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