Amazon Redshift Serverless
Get insights from data in seconds without having to manage data warehouse infrastructure
Benefits of Amazon Redshift Serverless
Get started with analytics in seconds
Experience high performance
Save costs and stay on budget
Why Amazon Redshift Serverless?
Easily run analytics workloads of any size without managing data warehouse infrastructure. Developers, data scientists, and data analysts can work across data warehouses and data lakes to build reporting and dashboarding applications, perform real-time analytics, collaborate on data, and build and train machine learning (ML) models. You pay only for what you use, so you save on costs. Amazon Redshift Serverless offers flexibility to support a diverse set of workloads of varying complexity, starting at a low price point. The new AI-driven scaling and optimization technology enables Amazon Redshift Serverless to automatically and proactively provision and scale data warehouse capacity, delivering fast performance for even the most demanding workloads. The system uses AI techniques to learn customer workload patterns across key dimensions, such as concurrent queries, query complexity, influx of data volume, and ETL patterns. It then continually adjusts resources throughout the day and applies tailored performance optimizations. These holistic and AI-enhanced techniques provide the best optimization for a given workload. You can set a desired price-performance target, and the data warehouse automatically scales to meet it. Load data and start querying right away in an easy-to-use, zero administration environment.
Use cases
Self-service analytics
Perform what-if analyses, anomaly detection, and ML-based forecasting, and get fast, actionable insights from your data.
Auto scaling for unpredictable workloads
No longer spend time determining compute capacity and encountering overspending or underserving as you run workloads with regular usage throughout the day and peaks of activity that involve complex, hard-to-predict queries.
New applications
Unsure of how to size your data warehouse when deploying a new data-driven application? Start an Amazon Redshift Serverless endpoint, and your data warehouse will be sized according to your workload requirements.
Auto scaling for variable workloads
Have applications with high variability in usage? Think of your HR, budgeting, and operational reporting applications. You no longer have to over- or under-provision capacity. Avoid overpaying, performance issues, and poor user experiences.
Multi-tenant applications
For multi-tenant applications with each tenant having specific busy and idle periods—depending on the time of day, year, promotional events, and so on—architect to use a workgroup for each tenant with a wide capacity range. Any workgroup can quickly scale up to handle periods of high activity.
Peloton aims to help people around the world reach their fitness goals through its connected fitness equipment and subscription-based classes. At Peloton, we collect and process a variety of data ranging from hardware sales to instructor trends and user workout data to create and refine our business decisions for better customer experiences. However, analytics workloads are becoming more complex, causing our database administrators to spend a lot more time in changing capacity thresholds and performing manual database optimizations. Leveraging the new optimizations capabilities in Amazon Redshift Serverless, we can eliminate even more of the data warehouse management tasks, making it more cost-efficient while delivering better performance.
Jerry Wang
Director of Data Engineering, Peloton
Our NextGen Population Health solution provides actionable insights directly to care teams via the aggregation and transformation of multisource data. Optimizing our systems to reduce manual interventions like setting up and managing data warehouse infrastructure is critical to our success. With Amazon Redshift Serverless, we’re no longer managing complex warehouse orchestration systems. Amazon Redshift Serverless has improved workload performance, and its auto-scaling capabilities allow us to use the speed of Amazon Redshift for even our most dynamic workloads while only paying for what we use. We're excited to migrate additional workloads to Amazon Redshift Serverless. It's a game changer.
Owen Zacharias
Vice President, Application Delivery, NextGen Healthcare
Enterprises are continuously looking for ways to leverage cloud platforms for analytics projects to reduce costs and increase productivity without sacrificing performance. With Amazon Redshift Serverless, data teams can start their cloud journey with convenient, easy-to-use, on-demand data warehousing and pay-as-you-go pricing. We're excited to leverage Amazon Redshift Serverless, which integrates natively with Matillion to help teams transform and sync data and accelerate time to insights regardless of scale.
Rob Cornell
Head of Cloud and Technology Alliances, Matillion
Ease of use and self-service data access is key for our analytics initiatives. With 'Amazon Redshift Serverless, we don't have to think about managing the data warehouse. Data from Amazon S3 gets loaded seven times faster than our previous solution, helping us get actionable insights from the millions of customer events loaded. We are thrilled with the performance improvements and cost optimizations we are seeing with Amazon Redshift Serverless.
Tomer Levi
Vice President of R&D, Sedric
Amazon Redshift Serverless helps us complete our data management without having to manage clusters and optimizes our cost by provisioning just the right amount of capacity to meet demand. Amazon Redshift Serverless is reducing the operational burden, lowering costs, and enabling scale for the Roche Go-to-Market domain. This simplification is a game changer, helping us rapidly onboard and support a variety of analytics-heavy use cases without friction.
Dr. Yannick Misteli
Lead Cloud Platform & ML Engineer, Roche
We're thrilled to include Amazon Redshift Serverless as an exciting addition to our data analytics workflow. This offering seamlessly replaces several parts of our previous infrastructure, and its simplicity makes it very easy to use. Amazon Redshift Serverless drastically helps reduce data engineering latency and acts as a force-multiplier in accelerating development. Implementing Amazon Redshift Serverless helped us cut through our data engineering backlog and now allows us to spend more of our time gathering insights from the data.
Harry Gollakota
Data Engineer, Huron