Amazon Redshift Connectors

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SQL ELT with Amazon Redshift V2 Connector

You can enhance the mapping performance with SQL ELT.

You can use SQL ELT to read data from a cloud data warehouse and write to the same cloud data warehouse. You can also read data from a data lake in your cloud ecosystem and write it to a cloud data warehouse in the same ecosystem.

Data Integration translates the transformation logic into ecosystem-specific commands and SQL statements that run in the underlying cloud infrastructure. This increases the data processing speed because the data isn't moved out of the cloud infrastructure for processing.

Example

You work in the sales and inventory department of a retail organization that operates multiple stores and an online platform. The management wants to optimize its inventory management, understand customer behavior, and improve sales strategies by analyzing data from various sources.

The organization uses Amazon S3 to store the data from multiple sources. To analyze the data, the organization plans to leverage the warehouse infrastructure of Amazon Redshift and load all its data to Amazon Redshift.

You can use SQL ELT to quickly load data from Amazon S3 to Amazon Redshift. SQL ELT is faster than the ETL method and eliminates any data egress costs as the data remains within your cloud infrastructure.

Part 2: Data Integration with Amazon Redshift V2 Connector

SQL ELT configuration options

SQL ELT query preview

Mappings in SQL ELT mode for Amazon Redshift

SQL ELT optimization for mapping tasks

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