Amazon Redshift Connector Best Practices

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Data Types and Load Patterns

You can read and write data of multiple types to an Amazon Redshift target.

The following lists the data types that you can write to an Amazon Redshift target:

Dimensional Data Warehouse: Dimensional data warehouse represents data as few facts, such as Invoice Amount and Ordered Quantity, or a large set of dimensions, such as Customer and Region. Each of the data are stored in their own tables with the artificial keys called as the surrogate keys.
Operational Data Store (ODS): ODS stores data mostly not in the raw form. The stored data either have the data as a system of record, a database where any business record is stored in its most original form, or a simplified data set for a quick analysis. ODS maintains the original transactional table structures.
Other data: These kind of data are very common for the users to store additional data along with a data warehouse that does not fit in either of the two types. However, these data are used either by analysis or other applications.

Informatica offers a wide array of transformations that support all the types of data and load patterns. One of the example is the Slowly Changing Dimension load pattern that are used for the important dimension tables. Informatica provides wizards, templates, and sample mappings that shows how to configure such patterns.

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Amazon Redshift Connector Best Practices

Amazon Redshift Connector Best Practices

Data Types and Load Patterns

Data Types and Load Patterns