The concept of data freshness is crucial in ensuring data quality by measuring how up-to-date the data is, which is vital for businesses relying on near real-time data to make informed decisions. Testing data sources for freshness, especially at the ingestion phase, helps detect issues early and assists in debugging by pinpointing whether problems originate at the source or during data transformation. Tools like dbt provide commands to test data source freshness, using a 'loaded_at_field' to compare timestamps against expected update frequencies, allowing error or warning alerts based on set thresholds. Implementing these tests requires careful consideration of each data source's update patterns to avoid alert fatigue, and it is recommended to run freshness tests before executing any data models. The results of these tests provide valuable insights and are stored in a 'sources.json' file, helping track the history of data freshness and identify recurrent issues.