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Amazon Redshift Source Integration

SourceReverse ETL

Amazon Redshift Source Integration

SourceReverse ETL

Easily set up Amazon Redshift as a Warehouse source on the RudderStack dashboard within minutes. Once successfully configured, RudderStack automatically ingests your specified events data from Redshift. You can then route your event data to your favorite third-party destinations supported by RudderStack.

By adding Amazon Redshift as a Source in the RudderStack dashboard, you can:

  • Synchronize with your Redshift data source based on the schedule and run frequency.
  • Securely ingest your customer event data and send them to your preferred destination platforms in the desired format.
Use the Amazon Redshift Source integration with popular destinations

Google Analytics

Analytics

Integrate with Google Tag Manager to track events and goals for conversion optimization

Google Analytics 4

Analytics

Analyze how customers interact with your product in real-time by tracking page scrolls, clicks, and even when they watch a video

What you can do with Amazon Redshift Source

Query and write data to and from your data lake using open formats including Parquet, ORC, JSON, Avro, CSV, and more

Create and train SageMaker models customers using SQL statements and use those models for predictions such as churn detection and risk scoring directly in their queries and reports using Redshift ML

Easily manage end-to-end analytics workflows using native integration with the AWS analytics ecosystem

Query petabytes of data in Amazon S3 without loading or transforming any data with the Redshift Spectrum feature

Share live data across Redshift clusters securely and easily

RudderStack Amazon Redshift Source Documentation

Refer to our step-by-step guide and start using the ReactNative SDK today

About Amazon Redshift Source

Amazon Redshift is the fastest and fully-managed cloud data warehouse. It allows you to handle large analytical workloads with high performance, speed, and efficiency. Redshift allows you to query and combine huge amounts of structured and semi-structured data while being cost-effective. As a result, you do not have to worry about the scale of your data or the cost associated with it.

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