"kedro.io.core.VersionNotFoundError: Did not find any versions for SparkDataSet". (The code works if I change the type for pandas with a small part of the data)

sh
2021-09-07 11:10:58,913 - kedro.runner.sequential_runner - WARNING - There are 1 nodes that have not run.
You can resume the pipeline run by adding the following argument to your previous command:
  --from-nodes "format_als_recomendations"
2021-09-07 11:10:59,449 - kedro.framework.session.store - INFO - `save()` not implemented for `BaseSessionStore`. Skipping the step.
Traceback (most recent call last):
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/bin/kedro", line 8, in <module>
    sys.exit(main())
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/framework/cli/cli.py", line 265, in main
    cli_collection()
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/framework/cli/cli.py", line 210, in main
    super().main(
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/home/malaguth/Workspace/Git/recommendation-engine/src/martins_advisor/cli.py", line 160, in run
    session.run(
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/framework/session/session.py", line 408, in run
    run_result = runner.run(filtered_pipeline, catalog, run_id)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/runner/runner.py", line 106, in run
    self._run(pipeline, catalog, run_id)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/runner/sequential_runner.py", line 90, in _run
    run_node(node, catalog, self._is_async, run_id)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/runner/runner.py", line 218, in run_node
    node = _run_node_sequential(node, catalog, run_id)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/runner/runner.py", line 313, in _run_node_sequential
    catalog.save(name, data)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/io/data_catalog.py", line 449, in save
    func(data)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/io/core.py", line 636, in save
    load_version = self.resolve_load_version()
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/io/core.py", line 575, in resolve_load_version
    return self._fetch_latest_load_version()
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/cachetools/decorators.py", line 73, in wrapper
    v = method(self, *args, **kwargs)
  File "/home/malaguth/Workspace/Envs/venv-recsys-dev/lib/python3.8/site-packages/kedro/io/core.py", line 558, in _fetch_latest_load_version
    raise VersionNotFoundError(f"Did not find any versions for {self}")
kedro.io.core.VersionNotFoundError: Did not find any versions for SparkDataSet(file_format=csv, filepath=/mnt/advisor-dev/data/07_model_output/als/recommendation, load_args={}, save_args={}, version=Version(load=None, save='2021-09-07T14.07.50.215Z'))

kedro run

yaml
_spark: &spark_parquet
  type: spark.SparkDataSet
  file_format: "parquet"

_spark_overwrite: &spark_overwrite
  <<: *spark_parquet
  save_args:
    mode: "overwrite"

SaleRefinedData:
  <<: *spark_overwrite
  filepath: ${spark_prefix}/data/03_refined/sale/
  layer: refined

SaleFeatureData:
  <<: *spark_parquet
  filepath: ${spark_prefix}/data/04_feature/sale/
  versioned: true
  layer: feature

python
def aggregate_sale_ratings(sdf: spark.DataFrame) -> spark.DataFrame:
    window = (
        Window
        .partitionBy(SaleDataModel.SKU, SaleDataModel.CLIENT_ID)
        .orderBy(SaleDataModel.DATE)
    )

    sdf = (
        sdf
        .withColumn(SaleFeatures.COUNT_RATING, F.dense_rank().over(window))
        .withColumn(SaleFeatures.BINARY_RATING, F.lit(1))
    )

    return sdf

bash
kedro run --pipeline sale_refined_to_feature --env dev