text: import contextlib
  import tempfile
  
  from google.cloud import bigquery
  
  from feast import BigQuerySource, FileSource
  from feast.data_format import ParquetFormat
  
  
  @contextlib.contextmanager
  def prep_file_source(df, event_timestamp_column=None) -> FileSource:
      with tempfile.NamedTemporaryFile(suffix=".parquet") as f:
          f.close()
          df.to_parquet(f.name)
          file_source = FileSource(
              file_format=ParquetFormat(),
              path=f.name,
              event_timestamp_column=event_timestamp_column,
          )
          yield file_source
  
  
  def simple_bq_source_using_table_ref_arg(
      df, event_timestamp_column=None
  ) -> BigQuerySource:
      client = bigquery.Client()
      gcp_project = client.project
      bigquery_dataset = "ds"
      dataset = bigquery.Dataset(f"{gcp_project}.{bigquery_dataset}")
      client.create_dataset(dataset, exists_ok=True)
      dataset.default_table_expiration_ms = (
          1000
          * 60
          * 60  # 60 minutes in milliseconds (seems to be minimum limit for gcloud)
      )
      client.update_dataset(dataset, ["default_table_expiration_ms"])
      table_ref = f"{gcp_project}.{bigquery_dataset}.table_1"
  
      job = client.load_table_from_dataframe(
          df, table_ref, job_config=bigquery.LoadJobConfig()
      )
      job.result()
  
      return BigQuerySource(
          table_ref=table_ref, event_timestamp_column=event_timestamp_column,
      )
  
  
  def simple_bq_source_using_query_arg(df, event_timestamp_column=None) -> BigQuerySource:
      bq_source_using_table_ref = simple_bq_source_using_table_ref_arg(
          df, event_timestamp_column
      )
      return BigQuerySource(
          query=f"SELECT * FROM {bq_source_using_table_ref.table_ref}",
          event_timestamp_column=event_timestamp_column,
      )
  