Data Source Freshness
dbt Cloud provides a helpful interface around dbt's source data freshness calculations. When a dbt Cloud job is configured to snapshot source data freshness, dbt Cloud will render a user interface showing you the state of the most recent snapshot. This interface is intended to help you determine if your source data freshness is meeting the SLAs that you've defined for your organization.
Enabling source freshness snapshots
First, make sure to configure your sources to snapshot freshness information.
Then, to enable source freshness snapshots in dbt Cloud, add a
dbt source freshness step to one of your jobs, or create a new job to snapshot source freshness. Note: If you're using an older version of dbt Core (before v0.21), you'll need to use the old name of this command instead:
dbt source snapshot-freshness. See
source command docs for details.
You can add
dbt source freshness anywhere in your list of run steps, but note that if your source data is out of date, this step will "fail', and subsequent steps will not run. dbt Cloud will trigger email notifications (if configured) based on the end state of this step.
If you do not want your models to run if your source data is out of date, then it could be a good idea to run
dbt source freshness as the first step in your job. Otherwise, we recommend adding
dbt source freshness as the last step in the job, or creating a separate job just for this task.
Another option is to select the source freshness checkbox in your execution settings when you configure a job on dbt cloud. Selecting this checkbox will run
dbt source freshness as the first step in your job, but it will not break subsequent steps if it fails. If you wanted your job dedicated exclusively to running freshness checks, you still need to include at least one placeholder step, such as
dbt build does not include source freshness checks when it builds and tests resources in your DAG. As such, here's a common pattern for defining jobs:
dbt buildas the run step
- check box for generating docs
- check box for source freshness
Source freshness snapshot frequency
It's important that your freshness jobs run frequently enough to snapshot data latency in accordance with your SLAs. You can imagine that if you have a 1 hour SLA on a particular dataset, snapshotting the freshness of that table once daily would not be appropriate. As a good rule of thumb, you should run your source freshness jobs with at least double the frequency of your lowest SLA. Here's an example table of some reasonable snapshot frequencies given typical SLAs:
|1 hour||30 mins|
|1 day||12 hours|
|1 week||About daily|
For more on exposing links to the latest documentation and sharing source freshness reports to your team, see Building and configuring artifacts.