@dashawn 

DLT treats the whole pipeline as one unit, so if any table definition throws an error during the planning phase (not just execution), the entire update fails. An empty S3 directory causing a schema inference failure is exactly the kind of thing that kills the whole run.

The most practical fix for the empty-source problem specifically is to add a guard in your table definition. If you're using Auto Loader, you can provide an explicit schema with cloudFiles.schemaLocation or cloudFiles.schema so Spark doesn't try to infer from an empty directory. That way the table definition stays valid even when there's nothing to read yet. It just processes zero rows.

For the broader "one table shouldn't tank the pipeline" concern, DLT doesn't have a built-in "skip on error" flag. It's a known pain point. What some teams do is split tables into separate pipelines grouped by criticality or source reliability, then orchestrate them through a Databricks Workflow. That way a flaky source only affects its own pipeline.

If splitting pipelines feels heavy-handed, you can also wrap the source read in a try/except within a Python DLT definition and return an empty DataFrame with the correct schema on failure. Something like:

@dlt.table
def my_table():
    try:
        return spark.readStream.format("cloudFiles") \
            .option("cloudFiles.format", "json") \
            .schema(my_schema) \
            .load("s3://bucket/path")
    except Exception:
        return spark.createDataFrame([], my_schema)

Not the prettiest, but it keeps the rest of the pipeline running. The table just stays empty until the source shows up.

One other thing worth knowing: Databricks recently added the ability to selectively refresh specific tables or retry just the failed tables from the pipeline UI. That doesn't prevent the initial failure, but it helps with recovery so you don't have to reprocess everything from scratch.

Please mark it as a Solution if this helps/resolves your issue so that others can benefit from it!