Use customer-managed encryption keys

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

Code sample

Java

To authenticate to Dataflow, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

// 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

To authenticate to Dataflow, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

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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