Skip to content

Data warehouse

| Article: 3 min read | Analytics

A data warehouse is a separate database into which data from all of a store's systems (site, analytics, advertising, CRM, warehouse, accounting) is regularly collected in a single format for reports and analysis. You need one when numbers from different systems have to be combined and spreadsheets no longer cope.

In plain words

The site, CRM, ad accounts and accounting system each have their own data and their own definitions. A warehouse brings them into one place where "order", "customer" and "revenue" mean one thing. It differs from the store's ordinary database in purpose: the database serves the site's daily work, while the warehouse is for analysis, so heavy reports do not slow the store down.

What it looks like technically

Google describes BigQuery as a fully managed cloud data warehouse with analytics tools: data is stored in a columnar format optimized for analytical queries, and works with SQL and Python without managing infrastructure. You can start for free, through the free tier or a sandbox; you pay for analysis (queries) and storage (Google Cloud documentation). There are other warehouses too; the name here is only an example of how it looks in practice.

GA4 can export raw, unsampled event data to BigQuery. The export is free within the sandbox, otherwise costs are possible; for a standard property the limit is 1 million events per day; streaming export costs an extra $0.05 per gigabyte (Google Analytics help). In the warehouse this data can be kept longer and combined with order and advertising data, which the GA4 interface itself does not do.

When you need a warehouse

The explanations below are practical criteria, not requirements from the source.

  • For one report you have to combine 3–4 sources by hand, and it takes hours every week.
  • Numbers in the CRM, analytics and accounting disagree, and nobody can explain why.
  • You need data for a period longer than analytics keeps (see GA4: up to 14 months for explorations).
  • You need segments by behavior and purchases together (RFM, cohorts).
  • You want to give an AI assistant clean data (AI for analysis).

When you don't

  • The store is small, there are 1–2 sources, and ready-made reports and a spreadsheet are enough.
  • Nobody will maintain the data flows: a warehouse without an owner quickly becomes yet another source of discrepancies.
  • The problem is not volume but the lack of metric definitions. Agree on those first.

Example (hypothetical)

A store with three sources: GA4, CRM and ad accounts. Every week the marketer spends two hours manually combining spend and orders by campaign. After the warehouse is connected, campaign data is collected automatically and the report in the dashboard updates daily. Saved time and fewer errors are the main benefit, not "better analytics".

Common mistakes

  • Collecting all the data "just in case" without questions it answers.
  • Not describing what each field means.
  • Ignoring personal data: access to the warehouse must be restricted (Personal data in Ukraine).
  • Not watching query costs: you pay for analysis and storage.

FAQ

Is a data warehouse the same as a database? No. A database serves the store's operations; a warehouse collects data from many systems for analysis.

Does a small store need a warehouse? Usually not, while there are 1–2 sources and ready-made reports.

Does it cost money? There is a free tier or sandbox (for example in BigQuery), but storage and queries beyond the limit are paid.

How is a warehouse different from a CDP? A CDP collects customer data for marketing, a warehouse for analyzing any data: see CDP.

Sources

Want to check this on your store?

Audit

We review the customer journey, catalog, technical state and data. A prioritized plan of changes in three days.

About the service →

Share

Related

  • Session recordings and heatmaps

    | Article: 3 min read

    A session recording is a replay of how one visitor used a site: where they moved the cursor, where they clicked, how they scrolled. A heatmap is a summary of such actions by many visitors on one page: where they click and how far they scroll. They show what…

  • LTV (customer lifetime value)

    | Article: 5 min read

    LTV (lifetime value, also CLV or CLTV) is the amount a customer brings to the store over the whole time of the relationship. For management decisions it is better to calculate it not in revenue but in margin: how much money the store keeps from all of the…

  • Incrementality and media mix models

    | Article: 3 min read

    Incrementality is the share of sales that advertising delivered on top of what the store would have received without it. If a person would have bought from you anyway, the ad added nothing with them, even though the report records them as a conversion. A…

  • CAC (customer acquisition cost)

    | Article: 5 min read

    CAC (customer acquisition cost) is the amount a store spends to get one new customer: all marketing and sales costs for a period divided by the number of customers who bought for the first time in that period.