Output to Amazon S3

Prerequisites

Ensure that you have an Amazon S3 connection with the correct permissions to write to your target bucket.

Create a job writing into Amazon S3

After you have fulfilled the prerequisites, you can create an INSERT job as follows:

CREATE SYNC JOB insert_into_s3
    START_FROM = BEGINNING
    FILE_FORMAT = (type = CSV)
    COMPRESSION = GZIP
    DATE_PATTERN = 'yyyy-MM-dd-HH-mm'
    RUN_INTERVAL = 1 MINUTE
    COMPUTE_CLUSTER = "Default Compute"
AS INSERT INTO S3 s3_output_connection 
    LOCATION = 's3://your-bucket-name/path/to/folder/'
    -- Use the SELECT statement to choose columns from the source and 
    --implement your business logic transformations.
    SELECT 
      column1 AS s3_column1, 
      MD5(column2) AS s3_column2 -- hash or mask columns using built-in functions
    FROM default_glue_catalog.your_schema.your_raw_data_table
    WHERE time_filter();

This example only demonstrates an example of all job options available when writing to Amazon S3. Depending on your use case, you may want to configure a different set of options. For example, set the FILE_FORMAT to Parquet or the COMPRESSION to SNAPPY

Alter a job writing to Amazon S3

Certain job options are considered mutable, meaning that in some cases, you can run a SQL command to alter an existing transformation job rather than having to create a new one.

For example, take the job we created as an example earlier:

CREATE SYNC JOB insert_into_s3
    START_FROM = BEGINNING
    FILE_FORMAT = (type = CSV)
    COMPRESSION = GZIP
    DATE_PATTERN = 'yyyy-MM-dd-HH-mm'
    RUN_INTERVAL = 1 MINUTE
    COMPUTE_CLUSTER = "Default Compute"
AS INSERT INTO S3 s3_output_connection 
    LOCATION = 's3://your-bucket-name/path/to/folder/'
    -- Use the SELECT statement to choose columns from the source and 
    -- implement your business logic transformations.
    SELECT 
      column1 AS s3_column1, 
      MD5(column2) AS s3_column2 -- hash or mask columns using built-in functions
    FROM default_glue_catalog.your_schema.your_raw_data_table
    WHERE time_filter();

If you wanted to keep the job as is but just change the cluster that is running the job, you can run the following command:

ALTER JOB insert_into_s3
    SET COMPUTE_CLUSTER = high_memory_cluster;

Note that some options such as RUN_INTERVAL or the FILE_FORMAT cannot be altered once the connection has been created.

Drop a job writing to Amazon S3

If you no longer need a certain job, you can easily drop it with the following SQL command:

DROP JOB insert_into_s3;

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Learn More

For the full list of job options with syntax and detailed descriptions, see the transformation job options for Amazon S3.

See the INSERT SQL command reference for more details and examples.