AQUA (Advanced Query Accelerator) is a new distributed and hardware-accelerated cache that enables Amazon Redshift to run up to 10x faster than other enterprise cloud data warehouses by automatically boosting certain types of queries. AQUA is available with the RA3.16xlarge, RA3.4xlarge, or RA3.xlplus nodes at no additional charge and with no code changes.

AQUA (Advanced Query Accelerator) for Amazon Redshift (1:54)

Challenges with existing data warehouse architectures

Network bandwidth as a bottleneck

Existing data warehousing architectures with centralized storage require data be moved to compute clusters for processing. As data warehouses continue to grow over the next few years, the network bandwidth needed to move all this data becomes a bottleneck on query performance.

Network bandwidth as a bottleneck

CPU in memory processing bottleneck

Since 2012, SSD storage throughput has increased 12x while the ability for CPUs to process data in memory has only scaled 2x. This means that even if you stopped the network from being the bottleneck, the ability for CPUs to process all this data is limited.

CPU in memory processing bottleneck

A new approach to data warehousing

AQUA takes a new approach to cloud data warehousing. AQUA brings compute to storage by doing a substantial share of data processing in place on the innovative cache. It uses AWS-designed processors and a scale-out architecture to accelerate data processing beyond anything traditional CPUs can do today.

AQUA data warehousing

Removes networking bandwidth limitations

AQUA accelerates Redshift queries by running data intensive tasks such as scans, filtering, and aggregation closer to the storage layer. This avoids networking bandwidth limitations by eliminating unnecessary data movement between data storage and compute clusters.

Powered by AWS-designed processors

AQUA uses AWS-designed processors with AWS Nitro chips adapted to speed up data encryption and compression, and custom analytics processors, implemented in FPGAs, to accelerate operations such as scans, filtering, and aggregation.

Scale-out-architecture

AQUA can process large amounts of data in parallel across multiple nodes, and automatically scales out to add more capacity as your storage needs grow over time.

Customer success

FOX
“FOX Corporation’s mission is to give millions of viewers the simple pleasure of being transported by a story on a screen. We have global audiences consuming premiere content across News, Sports, and Entertainment, and data is at the center of everything we do. Amazon Redshift empowers us to analyze petabytes of structured and semi-structured data across our data warehouse, operational database, and Amazon S3 data lake to discover, analyze, and activate data-driven decisions and powerful insights. As our petabyte-scale data continues to grow rapidly, we have been testing AQUA for Redshift to get better performance for our analytics queries while keeping our costs flat. We are seeing AQUA for Amazon Redshift improve the performance of some of our analytics queries by an order of magnitude and it is an example of how we are using latest technology to deliver a more personalized, curated, and timely experience to our viewers.”

Alex Tverdohleb, VP, Data Services - FOX Corporation

Accenture
“We use Amazon Redshift to build analytics applications for our customers that join data from multiple sources and empower users across the business, from data analysts to line-of-business leaders. As the data and demand for insights grows at an incredible pace, it is innovations from AWS, like AQUA for Amazon Redshift, that enable us to quickly meet the needs of our customers. AQUA has improved the performance of our most demanding analytic queries that scan large data sets by up to 10X helping us deliver timely insights to our ever-expanding customer base. This makes it easier for us to help our customers process more data in a timely and cost-effective manner and become data driven enterprises.”

A.K. Radhakrishnan, North America Data & AI AWS Lead - Accenture

Sisense
“Sisense's mission is to help businesses infuse analytics everywhere and empower their customers and employees to act on their data at the right time, every time. Thousands of enterprise companies and global brands rely on our AI-driven analytics platform to innovate, disrupt markets, and drive meaningful change in the world. We use Amazon Redshift to enable our customers to rapidly and easily transform complex data into highly interactive actionable apps that can be embedded and delivered at scale. We are using AQUA for Redshift and are delighted to see complex analytic queries that scan, filter, and aggregate large data sets run up to 8-10X faster than before. AQUA for Redshift gives us the performance and scalability needed to quickly analyze petabytes of data and deliver timely insights that benefit every user and every team.”

Guy Levy-Yurista, PhD, Chief Strategy Officer - Sisense

Amazon Advertising
“We use Amazon Redshift’s Lake House architecture (the ability to query data in the warehouse, operational databases, and data lake) to manage hundreds of petabytes of data and serve thousands of customers daily. We started using AQUA for Amazon Redshift recently, and it is a game changer. We have seen some of our most complex analytics queries related to attribution, personalization, brand insights, and aggregation that scan large data sets run up to 10X faster with AQUA. AQUA has dramatically reduced the average wait times for some of our most demanding queries enabling us to run 50% more queries on our system while keeping the cost the same resulting in faster time to value and better experience for our customers.”

Shamik Ganguly, Senior Manager - Amazon Advertising

Resources

Blog

New AQUA (Advanced Query Accelerator) for Amazon Redshift

Video

Introduction to AQUA for Amazon Redshift

Documentation

Working with AQUA

Documentation

When does Amazon Redshift use AQUA to run queries?

Get started with Amazon Redshift

Amazon Redshift getting started guide
Check out the getting started guide

Follow these steps to load sample data and start analyzing it with Amazon Redshift.

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