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BigQuery Schema: Project, Datasets and Tables
BigQuery Schema: Project, Datasets and Tables

See how your different data sources are organized on your BigQuery project.

Romulo Gomes avatar
Written by Romulo Gomes
Updated over a week ago

The Overall Structure

There are three layers of organization within your BigQuery project:

  1. Project

  2. Dataset

  3. Tables

In this page, we'll describe which layer, what it contains and what naming conventions are we using.

1) The Project

When you setup your BigQuery integration, you pick a project of your preference to store your data.

That's where all the data sources you connect are sent to:


2) The Dataset

A dataset is a collection of tables.

Each dataset equals one of the sources you connected under your Easy App Reports account (eg. Google Play, App Store, Meta Ads, etc).

Because your account can hold multiple sources, we had to give each dataset an unique name. We also had to make it readable as that's the name you'll see on Looker Studio or while if you're writing queries.

Here's the structure we're using:

ear[num]_[source]_[account_name]

Where:

  • account_name: your developer account name, truncated after 4 characters of each word (eg. "Meta Platform" becomes meta_plat);

  • source: the name of the source, such as appstore, googleplay, googleads, etc;

  • num: indicates the order in which you added this data source to your account.

Examples:

  • ear0_googleplay_meta_plat

  • ear1_appstore_meta_plat

  • ear2_metaads_meta_plat


3) The Tables

These follow the the same snake case model.

[abbreviated_source]_[table_name]

Examples:

  • gdc_installs: Google Developer Console - Installs

  • gdc_reviews: Google Developer Console - Reviews

  • apc_sales_trends: App Store Connect - Trends

  • apc_analytics_source: App Store Connect - Analytics per Source

And so on.

Notice that all table names are actually the report names as seen in this page.


If you found this useful you might want to have a look at our Data Freshness and How BigQuery works articles or this tutorial on how to migrate from Looker Studio to BigQuery data sources.

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