Of all the advantages of having a data warehouse in the cloud, the biggest advantage is that your data warehouse can be elastic. You can add or remove compute and storage as your demand grows and shrinks, as frequently as you need.
Amazon Redshift allows two major ways to resize your Amazon Redshift cluster—Classic resize and Elastic resize. Each offers different levels of flexibility and users get different downtime behavior. With Classic resize you can change the node type and number of nodes, but it stays read-only during the resizing. With Elastic resize, you can add or remove nodes, but you cannot change the node type. Also, the cluster is available during this operation to read or write. For more information on resizing clusters in Amazon Redshift visit the AWS resizing tutorial page. The image below shows how you can resize a cluster using Elastic resize.
Once you resize a cluster, Amazon Redshift redistributes data onto the available nodes. The way data is distributed varies between Classic and Elastic resizing.
If you use Informatica to interact with Amazon Redshift, you get the benefits of it immediately as soon as you process your next load or unload. We have rich connectivity to the AWS ecosystem. Informatica’s Cloud Amazon Redshift Connector uses AWS API to interact with Amazon S3 and Amazon Redshift. For more information visit Informatica’s AWS connectors page.
Informatica has the broadest connectivity both inside and outside AWS. This allows you to fetch data directly from various sources such as ERP, CRM, and API-based endpoints. It also allows you to load data from SaaS applications, such as Salesforce, as well as on-premises and cloud file storage. This data can be loaded to your data warehouse in Amazon Redshift either directly or after applying transformations as you load it.
Informatica has an easy-to-use tool called Cloud Mapping Designer. It helps you configure your tasks to read data from your sources directly and write it to Amazon Redshift after configuring any transformations you want to apply over it. (Watch: Learn how to get started with Cloud Mapping Designer.)
Informatica automatically optimizes its load or unload activities based on the available nodes and slices.
Similar options are available when you read data from Amazon Redshift. Most of these can be configured by users based on their understanding of data or their resources. You can configure highly efficient data loads to your data warehouse in Amazon Redshift using these. Using a combination of dynamic optimization using cluster size information and user-configurable parameters, Informatica allows users to take advantage of the elasticity which is vital to cloud data warehousing.