They can also directly query data in open formats, such as Parquet or ORC, in their Amazon S3 data lakes, as well as data in their operational databases, such as Amazon Aurora and Amazon RDS. They can create databases, schemas, tables, and load their own data from their desktop, Amazon Simple Storage Service (S3), via Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. ![]() ![]() Customers can take advantage of pre-loaded sample data sets along with sample queries to kick start analytics immediately. There is no need to choose node types, node count, or other configurations. Customers can launch a data warehouse and start analyzing the data with the Redshift Serverless option through just a few clicks in the AWS Management Console. The serverless option lets all users, including data analysts, developers, business users, and data scientists use Redshift to get insights from data in seconds by simply loading and querying data into the data warehouse. Redshift Serverless (in preview) makes it easy to run and scale analytics in seconds without having to provision and manage data warehouse clusters. We launched new features such as Redshift Serverless (in preview), Query Editor V2, and automated materialized views (in preview), as well as enhanced the Data API in 2021 to make it easier for customers to run their data warehouses. Let’s dive into each of these pillars and cover the key capabilities that we launched in 2021.Īmazon Redshift key innovations Redshift delivers easy analytics for everyoneĮasy analytics for everyone requires a simpler getting-started experience, automated manageability, and visual user interfaces that make is easier, simpler, and faster for both technical and non-technical users to quickly get started, operate, and analyze data in a data warehouse. So we continue to bring out new capabilities for performance at any scale. And finally, customers told us that they want the best price performance for analytics at any scale from Terabytes to Petabytes of data. So we continue to invest in letting customers analyze all of their data. Customers also told us that they want to break free from data silos and access data across their data lakes, databases, and data warehouses and analyze that data with SQL and machine learning (ML). Working backwards from customer requirements, we are investing in Redshift to bring out new capabilities in three main areas:Ĭustomers told us that the data warehouse users in their organizations are expanding from administrators, developers, analysts, and data scientists to the Line of Business (LoB) users, so we continue to invest to make Redshift easier to use for everyone. This post covers some of those features, including use cases and benefits. We continue to innovate with Redshift on our customers’ behalf and launched more than 50 significant features in 2021. ![]() Since then, we have launched capabilities such as Concurrency scaling, Spectrum, and RA3 nodes to help customers analyze all of their data and support growing analytics demands across all users in the organization. ![]() We announced Redshift in 2012 as the first cloud data warehouse to remove the complexity around provisioning, managing, and scaling data warehouses. Customers have asked for more capabilities in Redshift to make it easier, faster, and secure to store, process, and analyze all of their data. Amazon Redshift is the cloud data warehouse of choice for tens of thousands of customers who use it to analyze exabytes of data to gain business insights.
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