“The new capabilities announced today help us move customers toward a zero-ETL future on AWS, reducing the need to manually move or transform data between services. Many of our customers rely on multiple AWS database and analytics services to extract value from their data, and ensuring they have access to the right tool for the job is important to their success,” said Swami Sivasubramanian, vice president of Databases, Analytics, and Machine Learning at AWS. “The vastness and complexity of data that customers manage today means they cannot analyze and explore it with a single technology or even a small set of tools. To learn more about unlocking the value of data using AWS, visit /data. Together, these new capabilities help customers move toward a zero-ETL future on AWS. Customers can also now run Apache Spark applications easily on Amazon Redshift data using AWS analytics and machine learning (ML) services (e.g., Amazon EMR, AWS Glue, and Amazon SageMaker). Today’s announcement enables customers to analyze Amazon Aurora data with Amazon Redshift in near real time, eliminating the need to extract, transform, and load (ETL) data between services. company (NASDAQ: AMZN), today announced two new integrations that make it easier for customers to connect and analyze data across data stores without having to move data between services. LAS VEGAS-(BUSINESS WIRE)- At AWS re:Invent, Amazon Web Services, Inc. The preview is limited to one serverless endpoint for each AWS account and is not free but Amazon provides 500 USD in credits for compute, storage, and snapshot usage of Amazon Redshift Serverless.Amazon Aurora zero-ETL integration with Amazon Redshift enables customers to analyze petabytes of transactional data in near real time, eliminating the need for custom data pipelinesĪmazon Redshift integration for Apache Spark makes it easier and faster for customers to run Apache Spark applications on data from Amazon Redshift using AWS analytics and machine learning services The devil is of course in the pricing details, but yay!Ĭustomers pay separately for compute and storage on Redshift Serverless, with per-second billing and compute capacity measured in Redshift Processing Units (RPUs) while storage is charged in a similar way to a provisioned cluster using RA3 instances. Separately Amazon has announced Amazon Redshift ML, a new option for data analysts and database developers to create, train, and apply machine learning models using SQL commands.Ĭoncerned about the costs of the serverless options and the overlapping with Amazon Redshift Spectrum, Coney Quinn, cloud economist at The Duckbill Group, tweets:Īnd now there are Serverless options for RedShift (that isn't Spectrum?), EMR, MSK, and Kinesis. The serverless announcement will open it up to a whole new audience. mainly because it's terrifying, especially when you look at the cost. Mark Nunnikhoven, cloud strategist at Lacework, comments: Karan Desai, solution architect at AWS, tweeted:Īt this rate, by the end of his keynote, Adam Selipsky is going to announce that the company's name is being changed to Amazon Web Serverless. You can optionally specify the base data warehouse size to have additional control on cost and application-specific SLAs.Īs for the serverless documentation, most of the features supported by an Amazon Redshift provisioned cluster are also supported on a serverless endpoint but there are known issues and limitations during the preview, including the lack of public endpoints and support limited to a subset of availability zones and regions.ĭuring his first keynote at re:Invent, Adam Selipsky announced a serverless option for different analytics services, not only Redshift: Amazon EMR Serverless and Amazon MSK Serverless are now in public preview while Amazon Kinesis on-demand is generally available. As your demand evolves with more concurrent users and new workloads, your data warehouse scales seamlessly and automatically to adapt to the changes. Danilo Poccia, chief evangelist EMEA at AWS, explains:Īmazon Redshift Serverless automatically provisions the right compute resources for you to get started. The latest version of the managed data warehouse service targets deployments where it is difficult to manage capacity due to variable workloads or unpredictable spikes.Īmazon Redshift Serverless supports JDBC/ODBC-compliant tools and the Redshift Data API and is designed for sporadic workloads, development and test environments and ad-hoc business analytics, like anomaly detection or ML-based forecasting. As part of a trend towards serverless analytics options, AWS announced the public preview of Amazon Redshift Serverless.
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