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    How to Create a Data Source
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    Contents

    How to Create a Data Source

    • 4 Minutes to read
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    Article summary

    In the first step of our quickstart tutorial, we will guide you through creating a data source, which defines where you extract your data from and what kind of data it is.

    Prerequisites

    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

    1. Start Source Creation

    On your Dataddo dashboard, click on the Source Create link under Quick Actions. Select the service from which you want to extract your data.

    Create a source.

    2. Select Your Account

    In this step, select which account (e.g. Facebook Ads account) you want to extract data from. However, this depends on the connector type:

    Custom-Schema ConnectorFixed-Schema Connectors
    First, click on Add New Account at the bottom of the drop-down and authorize the connection of your service to Dataddo.

    Custom-schema connectors allow complete freedom when composing your data source metrics and attributes (e.g. Google Ads, Facebook Ads), which we will select in the next step.    		First, select a dataset (e.g. Stripe, Gusto), which is a pre-selected set of attributes. If you are not sure which dataset you need but know the metrics and attributes, use the Search by Name or Attribute function.

    Then, in the next step, click on Add New Account at the bottom of the drop-down and authorize the connection of your service to Dataddo.
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    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

    Next, let's select 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.

    Select metrics and attributes.

    Select Metadata in Advance Settings

    In this step, you can select what metadata you want to include. Dataddo allows the addition of the following fields to your data:

    Metadata FieldDescriptionUse
    Property IDThe unique identifier of the data property (such as an account, property, or data stream) from the original platform, like Google Analytics or Meta Ads.
    • Helps you trace each record back to its original source.
    • Crucial when you're working with multiple properties/accounts in the same dataset.
    • Makes filtering and segmentation by source easier in dashboards and reports.
    Dataddo Insert DateThe timestamp of when a particular record was ingested into the Dataddo system and written to your destination (like a data warehouse or BI tool).
    • Useful for troubleshooting and data freshness monitoring.
    • Enables time-based comparisons between ingestion and source data.
    • Can help detect issues like missing or delayed data syncs.
    Dataddo HashA unique value (like a fingerprint) generated from the values in each row. It’s typically a hash of key fields (like campaign ID, date, metric values, etc.).
    • Helps identify duplicate records.
    • Essential for change data capture (CDC) logic as it detects if a record has changed since the last sync.
    • Enables more efficient data deduplication and comparison operations in downstream systems.

    Select metadata.

    Finish Source Setup

    1. Schedule the Snapshotting

    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, you can choose a custom date range, and the exact time of data extraction.

    2. Allow Empty Data Extractions

    When your source connector attempts to extract data but finds no available rows (e.g., due to filters, date range, or the source being temporarily empty), Dataddo handles the situation based on the Allow Empty setting:

    • Allow Empty is enabled (default behavior): The source will not break. Instead, Dataddo will complete the run and simply extract zero rows. This is useful for workflows where temporary absence of data is expected (e.g., daily reports with no activity).
    • Allow Empty is disabled: The source will break and return an error. This is useful for alerting you when data is unexpectedly missing.

    3. Test Data and Save

    Finally, click on the Test Data button to preview and confirm your data and Save your configuration.

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    Testing your data is a great way to explore your data, especially when you're unsure what certain attributes represent. Since the table is populated with live data, it's easy to see real values and better understand each field before setting up your source.

    Well done, your new data source is ready!

    Next Steps

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    To extract and load historical data, see our articles on data backfilling.

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