Amazon Redshift offers leading price performance for diverse analytics workloads, whether dashboarding, application development, data sharing, or ETL (Extract, Transform, Load) jobs. With tens of thousands of customers running analytics on terabytes to petabytes of data, Amazon Redshift focuses on using performance telemetry from our large customer base to optimize performance for real-world customer workloads like high concurrency, low latency queries. Amazon Redshift is a self-learning, self-tuning system that delivers up to 5x better price performance than other cloud data warehouses and up to 7x better price-performance on high concurrency, low latency workloads. Keep the performance of your data workloads consistently high with Massively Parallel Processing (MPP) architecture, separation of storage and compute, Concurrency Scaling, machine learning led performance improvement techniques like short query acceleration, Auto-Materialized views, vectorized scans, Automatic Workload Manager (Auto WLM), and Automatic Table Optimization (ATO), to name a few. Access these innovations at no additional cost.
Scale linearly with predictable high performance
Scale data and compute up or down while keeping performance consistently high and costs predictable. Use the best-in-class hardware of the AWS Nitro System, with AutoMaterialized Views to auto-rewrite queries so they run faster, Automatic Table Optimization to tune table design, Automatic Workload Manager to offer dynamic concurrency and efficient resource utilization, Short Query Accelerator, and more.
Maximize your cost savings
Choose what’s right for your business needs, with pay-as-you-go, on-demand, and reserved instance pricing. With Amazon Redshift Serverless, pay only for what you use. Your data warehouse capacity automatically scales up or down to meet your analytics workload demands, and shuts down during periods of inactivity to save administration time and costs. With provisioned instances, pay for your database by the hour with no long-term commitments or upfront fees, or reduce your bill for overall steady-state usage — regardless of instance family — with reserved instance pricing.
Self-learning, self-tuning system to enhance performance
Seamlessly lower query latency for high concurrency analytics workloads and reduce manual intervention with machine learning (ML) features like Vectorized Querying techniques, string data performance enhancements, Short Query Accelerator, and more. Amazon Redshift’s Automatic Workload Manager simplifies workload management and maximizes query throughput by using ML to dynamically manage memory and concurrency for more efficient resource utilization. Automated Materialized Views rewrites thousands of queries every day for efficiency.
BI and Dashboarding workloads
When using dashboarding applications that are typically short queries and require high concurrent usage with ultrafast response time SLAs.
When building data-rich applications relying on analytics warehouses, several complex queries slice and dice data, including JOINs, UNIONs, nested SQL, and Window functions.
ETL (Extract, Transform, Load)
Batch or micro-batch workload where data from applications or data sources such as databases is transformed into formats and schemas for ingestion in to analytics warehouses.
Real-time streaming data analytics
Real-time analytics have demanding latency and throughput requirements for streaming data workloads that must be ingested into an analytics warehouse for real-time analytics and ML inferencing using pre-trained ML models. Additionally, complex ELT processes are implemented to manage downstream pipelines.
"Our world needs to go at least 3x faster in efficiency, electrification and decarbonization to fight climate change. At Schneider Electric, we play on both sides of the equation, leading by example in our own ecosystem while also providing solutions for our customers. Redshift is a key technology enabling us to get there, supporting thousands of users Enterprise wide, through Redshift concurrency scaling and RA3 nodes."
Aurelie Bergugnat, Chief Data Officer, Sr. Vice President, Data and Performance Management - Schneider Electric
“At RDG, data and analytics is essential to help our organization perform optimally. Business users want rapid and self-service access to data. They do not want to think about clusters and data warehouse management. Amazon Redshift’s serverless experience allows our users to be completely hands-off by managing the capacity provisioning, scaling and tuning of the data warehouse automatically, and delivering high performance for our data analyst users as well lowering our cost.”
Toby Ayre, Head of Data and Analytics - Rail Delivery Group
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