The freshness of your data is an important aspect of data quality. If data isn’t up-to-date, then it is stale. If your business thinks it's drawing insights from the most recent data, but really it's looking at data that’s a month old, there’s going to be problems.
The insights wouldn’t be accurate.
Your data models are only as insightful as the data is fresh. So, how do you ensure your data is always being ingested in a timely matter?
My favorite way to do this is using dbt freshness tests for my data models, specifically tests on my source data. When you focus on the most upstream data sources, you catch the issue at the source rather than downstream. It is much easier to debug and can save hours of your time.
So, let’s discuss how we can set up freshness tests at the source and how we can send these messages to Slack.