At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company, today announced three new serverless innovations across its database and analytics portfolio that make it faster and easier for customers to scale their data infrastructure to support their most demanding use cases. Today’s announcement introduces Amazon Aurora Limitless Database, a new capability that automatically scales beyond the write limits of a single Amazon Aurora database, making it easy for developers to scale their applications and saving them months compared to building custom solutions. Additionally, Amazon ElastiCache Serverless helps customers create highly available caches in under a minute and instantly scales vertically and horizontally to support customers’ most demanding applications, without needing to manage the infrastructure. AWS is also releasing a new Amazon Redshift Serverless capability that uses artificial intelligence (AI) to predict workloads and automatically scale and optimize resources to help customers meet their price-performance targets. These announcements build on AWS’s pioneering work with serverless technologies to help customers manage data at any scale and dramatically simplify their operations, so they can focus on innovating for their end users—without spending time and effort provisioning, managing, and scaling their data infrastructure.

“Since its earliest days, AWS has focused on removing undifferentiated heavy lifting for customers, and we have continued to build on that legacy through serverless offerings that dramatically simplify what it takes to build, run, and manage applications at scale,” said Dr. Swami Sivasubramanian, vice president of Data and Artificial Intelligence at AWS. “Data is the cornerstone of every organization’s digital transformation, and harnessing data to its full potential requires an end-to-end strategy that can scale with a customer’s needs while accommodating all types of use cases. The dynamic nature of data makes it perfectly suited to serverless technologies, which is why AWS offers a broad range of serverless database and analytics offerings that help support our customers’ most demanding workloads. The new serverless innovations announced today build on this foundation to make it easier for customers to scale to millions of transactions per second, quickly add capacity at a moment’s notice, and dynamically adapt workload patterns to optimize for performance and cost.”

Organizations create and store petabytes of data from a growing number of sources. To get the most value out of this data, these companies need an end-to-end strategy that can help them analyze and manage the data at any scale. Many AWS customers are already using a wide variety of purpose-built data services to support their most critical applications and make data-driven decisions, including Amazon Aurora for relational databases, Amazon ElastiCache for running in-memory caches, and Amazon Redshift for data warehousing. These services remove much of the heavy lifting that customers have to go through if they run their own database and analytics solutions, allowing them to focus on creating differentiated experiences for their end users. AWS continues to simplify operations for customers by releasing serverless technologies across its service portfolio, from some of AWS’s earliest offerings like Amazon Simple Storage Service (Amazon S3) to pioneering serverless, event-driven computing with AWS Lambda. Today, AWS offers the broadest set of serverless data analytics offerings in the cloud, making it easy for customers to take advantage of benefits like automatic provisioning, on-demand scaling, and pay-for-use pricing while using the right tool for the job. The new innovations announced today further AWS’s commitment to reimagining its database and analytics portfolio through serverless technologies, by making it even easier for customers to optimize costs and maximize their data’s value.

Amazon Aurora Limitless Database powers petabyte-scale applications with millions of writes per second

Today, hundreds of thousands of customers use Amazon Aurora, a fully managed MySQL- and PostgreSQL-compatible relational database that provides the performance and availability of commercial databases at up to one-tenth the cost. These organizations rely on Amazon Aurora Serverless v2 to power their applications because it is capable of scaling to support hundreds of thousands of transactions in a fraction of a second. As it scales, it adjusts capacity up and down in fine-grained increments to provide the right amount of database resources for the application. However, there are some use cases, such as online gaming and financial transaction processing, with workloads that need to process and manage hundreds of millions of global users, handle millions of transactions, and store petabytes of data. Today, these organizations must scale horizontally by splitting data into smaller subsets and distributing them across multiple distinct database instances in a process known as “sharding,” which requires months—or even years—of upfront developer effort to build custom software that routes requests to the correct instance or makes changes across multiple instances. Organizations also need to continuously monitor database activity and adjust capacity, which can be time-consuming and impact availability. The ongoing maintenance effort for these workloads is high, as organizations need to coordinate routine maintenance operations—such as adding a column to a table, taking consistent backups across all compute instances, or applying upgrades and patches—and constantly tune and balance the load across multiple instances. As a result, organizations need ways to automatically scale their applications beyond the limits of a single database without spending time building their scaling solutions.

Amazon Aurora Limitless Database scales to millions of write transactions per second and manages petabytes of data while maintaining the simplicity of operating inside a single database. Amazon Aurora Limitless Database automatically distributes data and queries across multiple Amazon Aurora Serverless instances based on a customer’s data model, eliminating the need to build custom software to route requests across instances. As compute or storage requirements increase, Amazon Aurora Limitless Database automatically scales resources vertically within serverless instances and horizontally across instances to meet workload demand, providing customers with consistently high performance while saving them months or years of effort in building custom software to scale their databases. Maintenance operations and changes can be made in a single database and automatically applied across instances, eliminating the need for managing routine tasks across dozens, or even hundreds, of database instances manually.

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Amazon ElastiCache Serverless makes it faster and easier to create a cache and instantly scale to meet application demand—without needing to provision, plan for, or manage capacity

Organizations building applications store frequently accessed data in caches to improve application response times and reduce database costs. These customers use open source, in-memory data stores like Redis and Memcached for caching because of their high performance and scalability. To simplify the process of building and running a cache, AWS offers Amazon ElastiCache, a fully managed Redis- and Memcached-compatible service that is used by hundreds of thousands of customers today for real-time, cost-optimized performance. Today, Amazon ElastiCache scales to hundreds of terabytes of data and hundreds of millions of operations per second with microsecond response times, and organizations use it to deploy highly available, mission-critical applications across multiple Availability Zones. While many organizations appreciate the fine-grained configuration options Amazon ElastiCache offers, some companies building a new application or migrating existing workloads want to get started quickly without designing and provisioning cache infrastructure, a process that requires specialized expertise and deep familiarity with application traffic patterns. Organizations also need to constantly monitor and scale their capacity to maintain consistently high performance, or overprovision for peak capacity, which results in excess costs. As a result, they need a solution that can help them manage the underlying infrastructure, making it faster and easier to create and operate a cache.

With Amazon ElastiCache Serverless, customers can now create a highly available cache in under a minute without infrastructure provisioning or configuration. Amazon ElastiCache Serverless eliminates the complex, time-consuming process of capacity planning by continuously monitoring a cache’s compute, memory, and network utilization and instantly scaling vertically and horizontally to meet demand without downtime or performance degradation. With Amazon ElastiCache Serverless, customers no longer need to rightsize or fine-tune their caches. Amazon ElastiCache Serverless automatically replicates data across multiple Availability Zones and provides customers with 99.99% availability for all workloads. Customers only pay for the data they store and the compute their application uses. Amazon ElastiCache Serverless is generally available today for both Redis- and Memcached-compatible deployment options.

Next-generation, AI-driven scaling and optimizations in Amazon Redshift Serverless deliver better price-performance for variable workloads

Tens of thousands of customers collectively process exabytes of data with Amazon Redshift every day. Many of these customers rely on Amazon Redshift Serverless, which automatically provisions and scales data warehouse capacity to meet demand based on the number of concurrent queries. While customers enjoy the ease of running analytics workloads of all sizes on Amazon Redshift Serverless without needing to manage data warehouse infrastructure, they would benefit further from the ability to easily adapt to changes in their workloads along additional dimensions, such as the amount of data or query complexity, to achieve consistently high performance while optimizing cost. For example, an organization with normally predictable dashboarding workloads may find that a new regulatory reporting requirement means they need to ingest substantially more data and handle more intensive, complex queries. To address workload changes along all dimensions, while ensuring consistent performance and without disrupting existing workloads, an experienced database administrator would have to spend hours separating the additional workload to a different data warehouse or making multiple, complex manual adjustments. This includes temporarily increasing the resources for data ingestion and new query workloads, pre-computing results for quick data access, organizing data for efficient retrieval, and timing data warehouse management tasks. All of these optimizations need to be done continuously, while managing each individual organization’s priorities for balancing performance and cost, regardless of changes in data volume, query complexity, or more concurrent queries.

With the new AI-driven scaling and optimizations, Amazon Redshift Serverless automatically scales resources up and down across multiple workload dimensions and performs optimizations to meet price-performance targets. Amazon Redshift Serverless uses AI to learn customer workload patterns along dimensions such as query complexity, data size, and frequency and continuously adjusts capacity based on those dynamic patterns to meet customer-specified, price-performance targets. Amazon Redshift Serverless now also proactively adjusts resources based on those customer workload patterns. For example, Amazon Redshift Serverless with AI-driven scaling and optimizations automatically lowers capacity during the day to handle dashboard workloads, but adds just the right amount of required capacity on demand whenever a complex query needs to be processed. Then overnight, Amazon Redshift Serverless proactively increases capacity again to support large data processing tasks without manual intervention. Building on existing self-tuning capabilities, Amazon Redshift Serverless automatically measures and adjusts resources and conducts a cost-benefit analysis to prioritize the best optimization for a given workload. Customers can set their own price-performance targets in the AWS Console, choosing to optimize between cost and performance. Amazon Redshift Serverless with AI-driven scaling and optimizations is available in preview.

Genesys is a leader in AI-powered experience orchestration that helps organizations engage with customers across any channel and empowers employees in the contact center and beyond. “At Genesys, we use Amazon ElastiCache to power high-throughput, low-latency storage for our all-in-one cloud platform, enabling millions of customer interactions per day,” said Rob Gevers, chief architect at Genesys. “We expect Amazon ElastiCache Serverless to help us improve performance and efficiency by eliminating the need to provision instances and choose specific configuration settings and scaling. With Amazon ElastiCache Serverless, we can remove administrative overhead and offer a significant leap in stability while providing the scalability we need to handle our growing usage and variable workloads.”

MIO Partners, Inc. is a global investment and advisory institution. “Our developers spend significant time evaluating usage, configuring node types, and designing cluster topologies to set up and configure cache capacity,” said Anand Mishra, chief technology officer at MIO Partners. “With Amazon ElastiCache Serverless, we can create a cache in less than a minute without any infrastructure provisioning, configuration, or capacity planning. Amazon ElastiCache Serverless eliminates the need for time-consuming capacity planning, improving our cost efficiencies and providing us with better operational reliability. Now, we can redeploy the team of engineers who were previously engaged in managing Redis to projects that deliver higher value for our clients.”

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,” said Jerry Wang, director of Data Engineering at Peloton. “However, analytics workloads are becoming more complex, causing our database administrators to spend a lot more time 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.”

Quantiphi is a digital engineering company driven by the desire to solve transformational problems. “At Quantiphi, we deliver tailored data analytics and machine learning solutions for our customers, and Amazon Redshift remains the cornerstone of our data warehouse services,” said Sanchit Jain, data and application practice lead at Quantiphi. “We have been hearing from our customers that they want a solution that can also help them meet price-performance within their budget constraints. The newly introduced AI-driven scaling and optimizations in Amazon Redshift Serverless will help improve our offering, bringing flexibility and intelligence to data management and ensuring automatic, cost-effective scaling based on historical query data. With this new capability, we can provide tailored solutions for our customers who seek optimal price-performance while adapting to ever-growing data volumes.”

Tuya Smart offers a cloud platform that connects devices via the Internet of Things (IoT) and empowers partners and customers by improving product value and making consumer lives more convenient through technology application. “Tuya’s IoT Developer Platform has over 846,000 registered developers from over 200 countries, serving more than 7,600 enterprises with Tuya IoT solutions,” said Chong Chen, head of Data Infrastructure at Tuya Smart. “We have been using Amazon Aurora, along with other AWS purpose-built databases, for more than five years, but we had to build our own in-house sharding and proxy solution for databases due to high write requests. We are excited that Amazon Aurora Limitless Database can help us bring our IoT platform performance and management to the next level by managing and scaling the write throughput we need to serve our increasing customers base while providing a consistent, smooth, and efficient response experience for our customers, all without us having to use a self-managed solution.”

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