Which data warehouse service can be used to query data in an Amazon S3 data lake without loading the data?

Gain up to 3x better price performance than other cloud data warehouses with automated optimizations to improve query speed.

RA3 instances: RA3 instances deliver up to 3x better price performance of any cloud data warehouse service. These Amazon Redshift instances maximize speed for performance-intensive workloads that require large amounts of compute capacity, with the flexibility to pay separately for compute independently of storage by specifying the number of instances you need. Learn more.

Advanced Query Accelerator (AQUA) for Amazon Redshift: AQUA 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 uses high-speed solid state storage, field-programmable gate arrays (FPGAs), and AWS Nitro to speed queries that scan, filter, and aggregate large datasets. AQUA is included with the Redshift RA3 instance type at no additional cost. Learn more.

Efficient storage and high-performance query processing: Amazon Redshift delivers fast query performance on datasets ranging in size from gigabytes to petabytes. Columnar storage, data compression, and zone maps reduce the amount of I/O needed to perform queries. Along with the industry-standard encodings such as LZO and Zstandard, Amazon Redshift also offers purpose-built compression encoding, AZ64, for numeric and date/time types to provide both storage savings and optimized query performance.

Limitless concurrency: Amazon Redshift provides consistently fast performance, even with thousands of concurrent queries, whether they query data in your Redshift data warehouse or directly in your Amazon S3 data lake. Amazon Redshift Concurrency Scaling supports virtually unlimited concurrent users and concurrent queries with consistent service levels by adding transient capacity in seconds as concurrency increases. Learn more.

Materialized views: Amazon Redshift materialized views allow you to achieve significantly faster query performance for iterative or predictable analytical workloads such as dashboarding and queries from Business Intelligence (BI) tools, and extract, transform and load ELT data processing jobs. You can use materialized views to easily store and manage precomputed results of a SELECT statement that may reference one or more tables, including external tables. Subsequent queries referencing the materialized views can run much faster by reusing the precomputed results. Amazon Redshift can efficiently maintain the materialized views incrementally to continue to provide the low latency performance benefits. Learn more.

Automated Materialized Views: Organizations are building more data dependent applications, dashboards, reports and ad-hoc queries than ever before. Each application needs to be tuned and optimized, which requires time, resources, and money. Materialized views are a powerful tool for improving query performance, and you could set these up if you have well understood workloads. However, you might have increased and changing workloads where query patterns are not predictable. Automated Materialized Views improve throughput of queries, lower query latency, shorten execution time through automatic refresh, auto query rewrite, incremental refresh, and continuous monitoring of Amazon Redshift clusters. Amazon Redshift balances the creation and management of AutoMVs with minimal resource utilization. Learn more.

Machine learning to maximize throughput and performance: Advanced ML capabilities in Amazon Redshift deliver high throughput and performance, even with varying workloads or concurrent user activity. Amazon Redshift uses sophisticated algorithms to predict and classify incoming queries based on their run times and resource requirements to dynamically manage performance and concurrency while also helping you prioritize your business-critical workloads. Short query acceleration (SQA) sends short queries from applications such as dashboards to an express queue for immediate processing rather than being starved behind large queries. Automatic workload management (WLM) uses ML to dynamically manage memory and concurrency, helping maximize query throughput. In addition, you can now easily set the priority of your most important queries, even when hundreds of queries are being submitted. Amazon Redshift is also a self-learning system that observes the user workload, determining the opportunities to improve performance as the usage grows, applying optimizations seamlessly, and making recommendations through Redshift Advisor when an explicit user action is needed to further turbocharge Redshift performance.

Result caching: Amazon Redshift uses result caching to deliver sub-second response times for repeat queries. Dashboard, visualization, and business intelligence tools that run repeat queries experience a significant performance boost. When a query runs, Amazon Redshift searches the cache to see if there is a cached result from a prior run. If a cached result is found and the data has not changed, the cached result is returned immediately instead of re-running the query.

Petabyte-scale data warehousing: With a few clicks in the console or a simple API call, you can easily change the number or type of nodes in your data warehouse, and scale up or down as your needs change. With managed storage, capacity is added automatically to support workloads up to 8 PB of compressed data. You can also run queries against petabytes of data in Amazon S3 without having to load or transform any data with the Amazon Redshift Spectrum feature. You can use S3 as a highly available, secure, and cost-effective data lake to store unlimited data in open data formats. Redshift Spectrum runs queries across thousands of parallelized nodes to deliver fast results, regardless of the complexity of the query or the amount of data.

Flexible pricing options: Amazon Redshift is the most cost-effective data warehouse, and you can optimize how you pay. You can start small for just $0.25 per hour with no commitments, and scale out for just $1,000 per terabyte per year. Amazon Redshift is the only cloud data warehouse that offers on-demand pricing with no upfront costs, Reserved Instance pricing that can save you up to 75% by committing to a one- or three-year term, and per-query pricing based on the amount of data scanned in your Amazon S3 data lake. Amazon Redshift’s pricing includes built-in security, data compression, backup storage, and data transfer. As the size of data grows, you use managed storage in the RA3 instances to store data cost-effectively at $0.024 per GB per month.

Predictable cost, even with unpredictable workloads: Amazon Redshift allows you to scale with minimal cost impact, as each cluster earns up to one hour of free Concurrency Scaling credits per day. These free credits are sufficient for the concurrency needs of 97% of customers. This provides you with predictability in your month-to-month cost, even during periods of fluctuating analytical demand.

Choose your node type to get the best value for your workloads: You can select from three instance types to optimize Amazon Redshift for your data warehousing needs: RA3 nodes, Dense Compute nodes, and Dense Storage nodes.

RA3 nodes let you scale storage independently of compute. With RA3, you get a high-performance data warehouse that stores data in a separate storage layer. You only need to size the data warehouse for the query performance that you need.

Dense Compute (DC) nodes allow you to create very high-performance data warehouses using fast CPUs, large amounts of RAM, and solid-state disks (SSDs) and are the best choice for less than 500 GB of data.

Dense Storage (DS2) nodes let you create large data warehouses using hard disk drives (HDDs) for a low price point when you purchase the three-year Reserved Instances. Most customers who run on DS2 clusters can migrate their workloads to RA3 clusters and get up to twice the performance and more storage for the same cost as DS2.

Scaling your cluster or switching between node types requires a single API call or a few clicks in the AWS Management Console. Visit the pricing page for more information.

Which of the following AWS services allows you to query data directly in Amazon S3?

Which service should I used to analyze data in Amazon S3 with SQL query?

Is S3 a data lake or data warehouse?

How do I query a S3 file?