Use customer-managed encryption keys

This sample shows how to use encryption keys managed by the customer, with a Dataflow pipeline.

Code sample

Java

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// Query from the NASA wildfires public dataset:
// https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=nasa_wildfire&t=past_week&page=table
String query =
    "SELECT latitude,longitude,acq_date,acq_time,bright_ti4,confidence "
    + "FROM `bigquery-public-data.nasa_wildfire.past_week` "
    + "LIMIT 10";

// Schema for the output BigQuery table.
final TableSchema outputSchema = new TableSchema().setFields(Arrays.asList(
    new TableFieldSchema().setName("latitude").setType("FLOAT"),
    new TableFieldSchema().setName("longitude").setType("FLOAT"),
    new TableFieldSchema().setName("acq_date").setType("DATE"),
    new TableFieldSchema().setName("acq_time").setType("TIME"),
    new TableFieldSchema().setName("bright_ti4").setType("FLOAT"),
    new TableFieldSchema().setName("confidence").setType("STRING")));

// Create the BigQuery options from the command line arguments.
BigQueryKmsKeyOptions options = PipelineOptionsFactory.fromArgs(args)
    .withValidation().as(BigQueryKmsKeyOptions.class);

// String outputBigQueryTable = "<project>:<dataset>.<table>";
String outputBigQueryTable = options.getOutputBigQueryTable();

// String kmsKey =
//    "projects/<project>/locations/<kms-location>/keyRings/<kms-keyring>/cryptoKeys/<kms-key>";
String kmsKey = options.getKmsKey();

// Create and run an Apache Beam pipeline.
Pipeline pipeline = Pipeline.create(options);
pipeline
    .apply("Read from BigQuery with KMS key",
        BigQueryIO.readTableRows()
            .fromQuery(query)
            .usingStandardSql()
            .withKmsKey(kmsKey))
    .apply("Write to BigQuery with KMS key",
        BigQueryIO.writeTableRows()
            .to(outputBigQueryTable)
            .withSchema(outputSchema)
            .withWriteDisposition(WriteDisposition.WRITE_TRUNCATE)
            .withKmsKey(kmsKey));
pipeline.run().waitUntilFinish();

Python

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import apache_beam as beam

# output_bigquery_table = '<project>:<dataset>.<table>'
# kms_key = 'projects/<project>/locations/<kms-location>/keyRings/<kms-keyring>/cryptoKeys/<kms-key>' # noqa
# beam_args = [
#     '--project', 'your-project-id',
#     '--runner', 'DataflowRunner',
#     '--temp_location', 'gs://your-bucket/samples/dataflow/kms/tmp',
#     '--region', 'us-central1',
# ]

# Query from the NASA wildfires public dataset:
# https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=nasa_wildfire&t=past_week&page=table
query = """
    SELECT latitude,longitude,acq_date,acq_time,bright_ti4,confidence
    FROM `bigquery-public-data.nasa_wildfire.past_week`
    LIMIT 10
"""

# Schema for the output BigQuery table.
schema = {
    'fields': [
        {'name': 'latitude', 'type': 'FLOAT'},
        {'name': 'longitude', 'type': 'FLOAT'},
        {'name': 'acq_date', 'type': 'DATE'},
        {'name': 'acq_time', 'type': 'TIME'},
        {'name': 'bright_ti4', 'type': 'FLOAT'},
        {'name': 'confidence', 'type': 'STRING'},
    ],
}

options = beam.options.pipeline_options.PipelineOptions(beam_args)
with beam.Pipeline(options=options) as pipeline:
    (
        pipeline
        | 'Read from BigQuery with KMS key' >>
        beam.io.Read(beam.io.BigQuerySource(
            query=query,
            use_standard_sql=True,
            kms_key=kms_key,
        ))
        | 'Write to BigQuery with KMS key' >>
        beam.io.WriteToBigQuery(
            output_bigquery_table,
            schema=schema,
            write_disposition=beam.io.BigQueryDisposition.WRITE_TRUNCATE,
            kms_key=kms_key,
        )
    )

What's next

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