Amazon Redshift Connectors

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Amazon Redshift targets

You can use an Amazon Redshift object as a single target in a

synchronization

task, mapping, or

mapping

task. You can also create an Amazon Redshift target based on the input source. When you use Amazon Redshift target objects, you can select a standard object as the primary source.

You can insert, update, upsert, and delete data from Amazon Redshift targets. An update or insert task writes an entire batch to an Amazon Redshift target if no errors occur within the batch. If an error occurs within a batch, the Secure Agent writes the entire batch to the error rows file.

When you configure the advanced target properties, you configure properties specific to Amazon Redshift. You can encrypt data, update statistical metadata of the database tables to improve the efficiency of queries, load data into Amazon Redshift from flat files in an Amazon S3 bucket, and use vacuum tables to recover disk space and sort rows in tables.

If a mapping includes a flat file or an Amazon Redshift target, you can choose to use an existing target or create a new target at run time. You must specify Amazon Redshift target object names in lowercase letters.

If the distribution key column in a target table contains null values and you configure a task with an upsert operation for the same target table, the task might create duplicate rows. To avoid creating duplicate rows, you must perform one of the following tasks:

Replace the null value with a non-null value when you load data.

Do not configure the column as a distribution key if you expect null values in the distribution key column.

Remove the distribution key column from the target table temporarily when you load data. You can use the Pre-SQL and Post-SQL properties to remove and then add the distribution key column in the target table.

Amazon Redshift sources and targets

Amazon Redshift staging directory for Amazon Redshift targets

Analyze target table

Data encryption in Amazon Redshift targets

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