Please provide a GCS bucket through custom_gcs_temp_location in the constructor of WriteToBigQuery or the fallback option -temp_location, or pass method="STREAMING_INSERTS" to WriteToBigQuery. Writing to BigQuery with FILE_LOADS method requires a GCS location to be provided to write files to be loaded into BigQuery. This new development opens the door to a lot of interesting use cases, given the widespread adoption and the flexibility that this format allows. Previously, one would have had to store the JSON data in a string column. However, I get the following error: ValueError: Invalid GCS location: None. Dev Genius 2 min read Earlier in 2022 BigQuery introduced native support for the JSON datatype. Write_disposition=beam.io.BigQueryDisposition.WRITE_TRUNCATE Table_(bigquery.TableFieldSchema(name="value", type="STRING"))ĭata | "Write to BigQuery" > beam.io.WriteToBigQuery(Ĭreate_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED, Table_(bigquery.TableFieldSchema(name="field", type="STRING")) | "Flatten JSON" > beam.ParDo(FlattenJsonFn()) | "Parse JSON" > beam.ParDo(ParseJsonFn()) In a column of type 'STRING': The JSON value is treated just like a normal string that happens to have JSON format. Here is the main.py file of my data pipeline (GCP Flex Template): import apache_beam as beam from apache_options import PipelineOptions, StandardOptions from apache_beam.io. import bigquery In BigQuery, JSON data may be stored in two ways: In a column of type 'RECORD': This data type is specifically designed to store nested structure data (JSON) in BigQuery. The JsonStreamWriter accepts data in the form of JSON records, and automatically converts. I would like to create a view table_b based on table_a that flattens source_data at the first level only. The BigQuery client library for Java provides the best of both worlds, through the JsonStreamWriter.
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