The general availability of Amazon Aurora PostgreSQL and Amazon DynamoDB zero-ETL integrations with Amazon Redshift.
The Amazon Aurora PostgreSQL-Compatible Edition zero-ETL integrations with Amazon Redshift are now generally available. By eliminating the need to create and maintain intricate data pipelines that carry out extract, transform, and load (ETL) activities, zero-ETL integration effortlessly makes transactional or operational data available in Amazon Redshift. It updates source data for you to use in Amazon Redshift for analytics and machine learning (ML) skills to extract timely insights and efficiently respond to important, time-sensitive events while automating source data replication to Amazon Redshift.
With these new zero-ETL integrations, you can conduct unified analytics on your data from various applications, eliminating the need to create and maintain separate data pipelines to write data from multiple relational and non-relational data sources into a single data warehouse.
Amazon Redshift is the target and a source is specified in order to construct a zero-ETL integration. The integration keeps an eye on the pipeline’s condition while replicating data from the source to the target data warehouse and making it easily accessible in Amazon Redshift.
Amazon Redshift integration of Aurora PostgreSQL zero-ETL
Near real-time analytics on petabytes of transactional data are made possible by the integration of Amazon Redshift and Amazon Aurora zero-ETL.
Why Aurora zero-ETL integration with Amazon Redshift?
Near real-time analytics and machine learning (ML) on petabytes of transactional data are made possible by the integration of Amazon Redshift with Amazon Aurora zero-ETL. Zero-ETL eliminates the need to create and maintain intricate data pipelines that carry out extract, transform, and load (ETL) activities by effortlessly making transactional data available in Amazon Redshift a few seconds after it was entered into Amazon Aurora.
Advantages
Access to data in almost real time
Run near real-time analytics and machine learning on petabytes of data by accessing transactional data from Aurora in Amazon Redshift in a matter of seconds.
Simple to use
Without having to create and maintain ETL pipelines to transfer transactional data to analytics platforms, you can quickly examine your transactional data in almost real time.
Smooth integration of data
To perform unified analytics across numerous apps and data sources, combine several tables from different Aurora database clusters and replicate your data to a single Amazon Redshift data warehouse.
Absence of infrastructure management
Using both Amazon Redshift Serverless and Amazon Aurora Serverless v2, you can do analytics on transactional data in almost real-time without managing any infrastructure.
Use cases
Operational analytics in near real time
To effectively respond to important, time-sensitive events, use Amazon Redshift analytics and machine learning capabilities to extract insights from transactional and other data in almost real-time. For use cases like fraud detection, data quality monitoring, content targeting, better gaming experiences, and customer behavior research, near real-time analytics can help you obtain more precise and timely insights.
Large-scale analytics
Petabytes of your transactional data pooled from several Aurora database clusters can be analyzed using Amazon Redshift’s capabilities thanks to the Aurora zero-ETL connector. You can benefit from Amazon Redshift’s extensive analytical features, including federated access to numerous data stores and data lakes, materialized views, built-in machine learning, and data sharing. With Amazon Redshift ML’s native integration into Amazon SageMaker, you can use simple SQL queries to generate billions of predictions.
lessen the operational load
It is frequently necessary to create, oversee, and run a sophisticated data pipeline ETL system in order to move data from a transactional database into a central data warehouse. You may easily transfer the schema, current data, and data modifications from your Aurora database to a new or existing Amazon Redshift cluster with a zero-ETL integration. Complex data pipeline management is no longer necessary with zero-ETL integration.
How to begin
You designate an Amazon Redshift data warehouse as the target and an Aurora DB cluster as the data source when creating your zero-ETL interface between Aurora and Redshift. Data from the source database is replicated into the target data warehouse through the integration. Within seconds, the data is accessible in Amazon Redshift, enabling data analysts to start utilizing the analytics and machine learning features of the platform.
Cost
Aurora zero-ETL integration with Amazon Redshift is free of charge via AWS. The change data produced by a zero-ETL integration is created and processed using pre-existing Aurora and Amazon Redshift resources, which you pay for. These resources could consist of:
- By turning on change data capture, more I/O and storage are used.
- For the first data export to seed your Amazon Redshift databases, the snapshot export costs
- Extra Amazon Redshift storage for data replication
- Extra Amazon Redshift computation for data replication processing
- Cross-AZ data transfer fees for transferring data between sources and destinations.
Continuous data change processing via zero-ETL integration is provided at no extra cost. Please visit the Aurora price page for additional details.
Availability
The AWS regions for the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Hong Kong), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm) now offer Aurora PostgreSQL zero-ETL integration with Amazon Redshift.
