How do you enforce data freshness in dbt?

Enforcing data freshness in dbt primarily involves implementing tests that verify timestamps and other freshness metrics. This practice is crucial because it ensures that the data being used in analyses is current and accurately reflects the most recent state of the source data.

With freshness tests, you can define specific criteria that dictate how old the data can be before it is considered stale. These tests check for the recency of timestamps or other indicators of data age, allowing you to quickly identify and address any issues with outdated information. When discrepancies arise, these tests can trigger notifications or alert data teams to investigate, ensuring that the analytics produced from the dbt models are based on reliable and timely data.

In contrast, while scheduling regular runs in a production environment is a common practice in dbt, it does not inherently measure or enforce data quality regarding freshness. Regular runs are important for workflow efficiency but do not provide mechanisms to ensure that the data meets freshness criteria.

Archiving old data regularly addresses data management concerns but does not directly relate to the freshness of the data being analyzed. This practice is more about data lifecycle management rather than ensuring that active datasets are up-to-date.

Creating new tables for fresh data daily may contribute to ensuring that fresh datasets are available, but this approach could lead to