dbt source command provides subcommands that are useful when working with source data. This command provides one subcommand,
dbt source freshness.
If you're using an older version of dbt Core (before v0.21), the old name of the
freshness subcommand was
snapshot-freshness. (It has nothing to do with snapshots, which is why we renamed it.) Each time you see the command below, you'll need to specify it as
dbt source snapshot-freshness instead of
dbt source freshness.
dbt source freshness
If your dbt project is configured with sources, then the
dbt source freshness command will query all of your defined source tables, determining the "freshness" of these tables. If the tables are stale (based on the
freshness config specified for your sources) then dbt will report a warning or error accordingly. If a source table is in a stale state, then dbt will exit with a nonzero exit code.
Specifying sources to snapshot
dbt source freshness will calculate freshness information for all of the sources in your project. To snapshot freshness for a subset of these sources, use the
# Snapshot freshness for all Snowplow tables:
$ dbt source freshness --select "source:snowplow"
# Snapshot freshness for a particular source table:
$ dbt source freshness --select "source:snowplow.event"
Configuring source freshness output
dbt source freshness completes, a JSON file containing information about the freshness of your sources will be saved to
target/sources.json. An example
sources.json will look like:
To override the destination for this
sources.json file, use the
# Output source freshness info to a different path
$ dbt source freshness --output target/source_freshness.json
Using source freshness
Snapshots of source freshness can be used to understand:
- If a specific data source is in a delayed state
- The trend of data source freshness over time
This command can be run manually to determine the state of your source data freshness at any time. It is also recommended that you run this command on a schedule, storing the results of the freshness snapshot at regular intervals. These longitudinal snapshots will make it possible to be alerted when source data freshness SLAs are violated, as well as understand the trend of freshness over time.
dbt Cloud makes it easy to snapshot source freshness on a schedule, and provides a dashboard out of the box indicating the state of freshness for all of the sources defined in your project. For more information on snapshotting freshness in dbt Cloud, check out the docs.