If you see that the incremental loading is not working as expected and the incremental values are not modified between pipeline runs, check the following:

1. Make sure the destination, pipeline_name, and dataset_name are the same between pipeline runs.

2. Check if dev_mode is False in the pipeline configuration. Check if refresh for associated sources and resources is not enabled.

3. Check the logs for the Bind incremental on <resource_name> ... message. This message indicates that the incremental value was bound to the resource and shows the state of the incremental value.

4. After the pipeline run, check the state of the pipeline. You can do this by running the following command:

dlt pipeline -v <pipeline_name> info

For example, if your pipeline is defined as follows:

@dlt.resource
def my_resource(
    incremental_object = dlt.sources.incremental("some_key", initial_value=0),
):
    ...

pipeline = dlt.pipeline(
    pipeline_name="example_pipeline",
    destination="duckdb",
)

pipeline.run(my_resource)

You'll see the following output:

Attaching to pipeline <pipeline_name>
...

sources:
{
  "example": {
    "resources": {
      "my_resource": {
        "incremental": {
          "some_key": {
            "initial_value": 0,
            "last_value": 42,
            "unique_hashes": [
              "nmbInLyII4wDF5zpBovL"
            ]
          }
        }
      }
    }
  }
}

Verify that the last_value is updated between pipeline runs.

Type mismatch errors#

If you encounter an IncrementalCursorInvalidCoercion error, it typically means the initial_value type does not match the data type of the field in your source data.

Example#

This fails because the initial_value is an integer, but the created_at values are string-formatted timestamps:

# This fails: integer initial_value with string timestamps
@dlt.resource
def my_data(
    created_at=dlt.sources.incremental("created_at", initial_value=9999)
):
    yield [{"id": 1, "created_at": "2024-01-01 00:00:00"}]

To fix this, use a string timestamp that matches the format of the source field:

created_at = dlt.sources.incremental("created_at", initial_value="2024-01-01 00:00:00")

To avoid similar issues:

• Always ensure the initial_value type matches the data type in the source field.
• If the field requires transformation, apply add_map to convert the type before incremental tracking.
• Use a separate column if needed to retain the original format for downstream processing or reference.