PowerExchange for Amazon Redshift User Guide

10.5.6
- 10.5.4
- 10.5.3
- 10.5.2
- 10.5
- 10.4.1
- 10.4.0

Back Next

Amazon Redshift Data Object Read Operation Properties

The Data Integration Service reads data from Amazon Redshift based on the data object read operation.

The Developer tool displays advanced properties for the Amazon Redshift data object operation in the Advanced view.

The following table describes the Advanced properties for an Amazon Redshift data object read operation:

Property	Description
S3 Bucket Name	Amazon S3 bucket name for staging the data. You can also specify the bucket name with the folder path. If you provide an Amazon S3 bucket name that is in a different region than the Amazon Redshift cluster, you must configure the REGION attribute in the Unload command options. Required if you configure a read operation enabled for staging data. Optional when you do not configure staging for the read operation. The S3 bucket name doesn't appear in the logs of the data preview table in Redshift.
Enable Compression	Compresses the staging files into the Amazon S3 staging directory. The mapping performance improves when the Data Integration Service compresses the staging files. Applicable when you run a mapping in the native environment, on the Spark engine, or on the Databricks Spark engine.
Staging Directory Location	Amazon Redshift staging directory. Specify a directory on the machine that hosts the Data Integration Service. Applicable when you run a mapping in the native environment.
Unload Options	Unload command options. Add options to the Unload command to write data from an Amazon Redshift object to an S3 bucket. You can add the following options: DELIMITER REGION PARALLEL NULL AWS_IAM_ROLE ADDQUOTES Specify a directory on the machine that hosts the Data Integration Service. Do not use PARALLEL OFF if you run a mapping on the Blaze engine.
Treat NULL Value as NULL	Retains the null values when you read data from Amazon Redshift. Applicable when you run a mapping in the native environment.
Encryption Type	Method you want to use to encrypt data. Select one of the following values: None SSE-S3 SSE-KMS CSE-SMK For more information, see Data Encryption in Amazon Redshift Sources.
Download S3 Files in Multiple Parts	Downloads large Amazon S3 objects in multiple parts. When the file size of an Amazon S3 object is greater than 8 MB, you can choose to download the object in multiple parts in parallel. Applicable when you run a mapping in the native environment.
Multipart Download Threshold Size	Maximum size of an Amazon S3 object in bytes. When you download large Amazon S3 objects, the large objects are broken into multiple parts. Default is 5 MB. Applicable when you run a mapping in the native environment.
Pre-SQL	The pre-SQL commands to run a query before you read data from Amazon Redshift. You can also use the UNLOAD or COPY command. The command you specify here is processed as a plain text. Applicable when you run a mapping in the native environment, on the Spark engine, or on the Databricks Spark engine.
Post-SQL	The post-SQL commands to run a query after you read data to Amazon Redshift. You can also use the UNLOAD or COPY command. The command you specify here is processed as a plain text. Applicable when you run a mapping in the native environment, on the Spark engine, or on the Databricks Spark engine.
Select Distinct	Selects unique values. The Data Integration Service includes a SELECT DISTINCT statement in the query if you choose this option. Amazon Redshift ignores trailing spaces. Therefore, the Data Integration Service might extract fewer rows than expected. Applicable when you run a mapping in the native environment, on the Spark engine, or on the Databricks Spark engine.
SQL Query	Overrides the default SQL query. Enclose column names in double quotes. The SQL query is case sensitive. Specify an SQL statement supported by the Amazon Redshift database. When you specify the columns in the SQL query, ensure that the column name matches the source column name that are mapped in the mapping. When you use the date and timestamp data types, you need to specify the format YYYY-MM-DD HH24:MI:SS.US to take care of the data conversion. For example, for a Redshift table sample that contains the fields d_date of the date data type and t_timestamp of the timestamp data type, specify the SQL query in the following format: select (to_char(cast ( sample.d_date as timestamp ),'YYYY-MM-DD HH24:MI:SS.US')), (to_char(sample.t_timestamp,'YYYY-MM-DD HH24:MI:SS.US')) FROM sample; Applicable when you run a mapping in the native environment, on the Spark engine, or on the Databricks Spark engine.

Rename Saved Search

Table of Contents

PowerExchange for Amazon Redshift User Guide

PowerExchange for Amazon Redshift User Guide

Amazon Redshift Data Object Read Operation Properties

Amazon Redshift Data Object Read Operation Properties