Well, in the case that you are unable to update Delta Live Tables (DLT) in Databricks, here is something that could be possibly the common issues and troubleshooting steps:
Possible Reasons & Fixes:
Schema Enforcement Issues—DLT doesn't allow schema integrity violations. A change in the schema, like adding a new column or mismatching data types, requires auto-merge (set spark. data bricks.delta.schema. auto-merge.enabled = true) or schema evolution defined in your pipeline.
Pipeline Failures—You can check for failure logs in the DLT event logs (SHOW LIVE TABLE EVENTS). Also, make sure that the source data is accessible, transformations have been initiated correctly, and dependent tables are available for consumption.
Data Refresh & Constraints
If changes made do not reflect, check if your DLT mode is Append Only (which does not allow any updates) or Complete, which triggers a full refresh. For an incremental update, ensure the expectations and apply_changes settings align with your requirements.
Additional Debugging Steps:
Check Cluster Logs: Look for errors in the cluster UI → Logs → Driver/Executor Logs.
Run Queries Manually: Validate transformations using SELECT * FROM before updating DLT.
Validate Source Tables: Delta Lake tables are accessible and correctly formatted or streaming sources.
Test with a Small Sample: If dealing with a large dataset, conduct a test with a limited number of records to isolate problems.