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Creating a data source takes you through five steps, shown in the progress bar at the top of the wizard. Each step is explained below.
1. Pick the connector
On the Sources page, click Create Source , then select the connector from the catalog. Use the search bar or the category tabs if you do not see it right away. You can rename the source at any time using the pencil icon next to its name.
2. Choose the account
This step selects what Dataddo reads from.
* Authorizer: Select an account you have already authorized from the drop-down. If you have none yet, choose Add new account and follow the prompts. If no authorizer is selected, Dataddo asks you to authorize before you continue.
* What to extract from: Select the exact entity you want to pull data from. Depending on the service this may be labelled an account, property, profile, workspace, or similar, sometimes with a sub-level to choose as well.
* Multiple accounts: To pull the same data from every entity you can access, turn on Automatically collect data from all ... This is multi-account extraction. Leave it off to choose them by hand.
3. Build the data model
Choose exactly what to extract. What you can pick depends on the connector, and the wizard may group the fields differently for each one.
* Values: the measures you want, often called metrics, such as sessions, clicks, or revenue.
* Breakdowns: the fields you group or split those values by, often called dimensions or attributes, such as date, country, or campaign.
* Context metadata (when available): identifier fields that Dataddo derives from your selection, such as the account or property ID. They let you tell rows apart when you combine several sources.

Some connectors limit which fields can be queried together; the wizard flags this where it applies. Click Test on Sample Data at any point to preview the result before you continue.
4. Add metadata columns (optional)
Two optional columns help your destination handle the data.
* Dataddo Hash (Include Row Hash): a fingerprint built from the columns you pick. It works as a natural key, so your destination can deduplicate rows and run upserts instead of creating duplicates. Turn it on, then select the columns that uniquely identify a row.
* Dataddo Extraction Timestamp: the time each row was extracted. Use it to watermark the data, for example to build slowly changing dimensions or to track when a value last changed.

5. Set the schedule
Decide how often Dataddo runs the extraction.
* Frequency: how often the pipeline runs, for example daily. Click Show advanced settings to also set the exact hour and minute (UTC).
* Date range: the relative window each run extracts, for example "Yesterday". The window moves forward on every run.
* Historical data: a new source starts from the current window. To load older data, run a full data re-sync after the source is created.
* Allow Empty Data Extractions: when on, a run that returns no data records zero rows instead of failing. Turn it on if the source can legitimately have periods with no data.

Click Save. Your data source is ready.

Para saber mais sobre fortuine 500, consulte fortuine 500 e veja as orientações disponíveis.