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Output to an Amazon Redshift table
Ensure that you have a Redshift connection with the correct permissions to write to your target table. Additionally, this target table and columns should already exist within Redshift before writing to it using SQLake.
You also need a storage connection that has access to the bucket you would like the job to use to store the intermediate files used while running the job.
Finally, you should also have a staging table created previously that contains the data you intend to write to Redshift.
Once you have fulfilled the prerequisites, you can create an
INSERTjob as follows:
CREATE JOB load_data_to_redshift
START_FROM = BEGINNING
SKIP_FAILED_FILES = TRUE
FAIL_ON_WRITE_ERROR = FALSE
AS INSERT INTO REDSHIFT <redshift_connection>.<schema_name>.<target_table_name> MAP_COLUMNS_BY_NAME
SELECT orderid AS app_name
This example only uses a subset of all job options available when writing to Redshift. Depending on your use case, you may want to configure a different set of options.