You can replicate data from multiple Amazon Aurora databases into the same Amazon Redshift instance to run analytics across multiple applications. Data is then replicated into your data warehouse within seconds after transactional data is written into Amazon Aurora, eliminating the need to build and maintain complex data pipelines. With this capability, you can choose the Amazon Aurora databases containing the data you want to analyze with Amazon Redshift. With Amazon Aurora zero-ETL integration with Amazon Redshift, you can use Amazon Redshift for near real-time analytics and machine learning on petabytes of transactional data stored on Amazon Aurora MySQL databases (available in limited preview).Amazon Redshift automates file ingestion and takes care of data-loading steps under the hood. In this way, you don’t need to manually or repeatedly run copy procedures. The files can use any of the formats supported by the Amazon Redshift copy command, such as CSV, JSON, Parquet, and Avro. With this new capability, Amazon Redshift automatically loads the files that arrive in an Amazon Simple Storage Service (Amazon S3) location that you specify into your data warehouse. Amazon Redshift now supports auto-copy from Amazon S3 (available in preview).In this blog, I introduce some of these new features that fit into two main categories: This year at re:Invent, Amazon Redshift has announced a number of features to help you simplify data ingestion and get to insights easily and quickly, within a secure, reliable environment. Additionally, data warehouses are increasingly becoming mission critical systems that require high availability, reliability, and security.Īmazon Redshift is a fully managed petabyte-scale data warehouse used by tens of thousands of customers to easily, quickly, securely, and cost-effectively analyze all their data at any scale. These data pipelines are costly, and the delays can lead to missed business opportunities. As a consequence of this complex setup, it can take data engineers weeks or even months to build data ingestion pipelines. This requires them to build manual data pipelines spanning across their operational databases, data lakes, streaming data, and data within their warehouse. A common pattern with data-driven organizations is that they have many different data sources they need to ingest into their analytics systems. When we talk with customers, we hear that they want to be able to harness insights from data in order to make timely, impactful, and actionable business decisions.
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