- 2 Minutes to read
How to Create a Data Source
- 2 Minutes to read
In the first step of our quickstart tutorial, we will guide you through creating a data source, which allows you to choose the specific data to extract from your service.
Make sure your account (e.g. Facebook account or GA4 account) has permissions to access and extract your data. Usually, at least admin-level permissions are required.
Configure Your Data Source
- In Dataddo, click on the Create a Source button under Quick Actions. Select the service from which you want to extract your data.
- If your connector has a fixed schema, select your dataset.
- Fixed-schema connectors offer pre-selected set of attributes aka datasets (e.g. Stripe, Gusto). If you are not sure which dataset you need but know the metrics and attributes, use the Search by Name or Attribute function.
- Custom-schema connectors allow complete freedom when composing your data source (e.g. Google Ads, Facebook Ads).
- Click on Add New Account at the bottom of the drop-down and authorize the connection of your service to Dataddo.
When you add a new account, you also create a new authorizer. You can always add more authorizers or accounts later.
Select Metrics, Dimensions, or Attributes
In this step, select your metrics, dimensions, or attributes to specify what data you want to extract. In short, you are defining the actual data model of the source and choosing what columns will be included in the data source.
Configure Snapshotting and Finish Creating Your Data Source
- Select the frequency of your snapshotting. Snapshots determine how often your data should be extracted. Recommended default is daily snapshotting to make sure you have up-to-date data.
- [Optional] In the Advanced Settings, choose a custom date range, and the exact time of data extraction.
- Click on the Test Data button to preview and confirm your data.
- Save your configuration.
Well done, your new data source is ready!
Test Data is also a great feature for data exploration when you are unsure about what certain attributes mean. As your table will be populated with live data, it will be easy to discover values for each attribute.
If you want to extract and load historical data, see our articles on data backfilling.