AWS Cloud Databases
High-performance, secure, and reliable foundation to power generative AI solutions and data-driven applications at any scale
Why AWS databases?
AWS databases offer a high-performance, secure, and reliable foundation to power generative AI solutions and data-driven applications that drive value for your business and customers. AWS high-performance databases support any workload or use case, including relational databases with 3-5x faster throughput than alternatives, purpose-built databases with microsecond latency, and built-in vector database capabilities with fastest throughput at the highest recall rates. AWS provides serverless options that remove the need to manage capacity by instantly scaling on demand. AWS databases deliver unmatched security with encryption at rest and in transit, network isolation, authentication, resolution of anomalies, and rigorous adherence to compliance standards. They are highly reliable because the data is automatically replicated across multiple Availability Zones within an AWS Region. With 15+ database engines optimized for the application’s data model, AWS fully managed databases remove the undifferentiated heavy lifting of database administrative tasks.
Benefits of AWS cloud databases
Enabling multicloud strategies
Many of AWS data services enable multicloud strategies. We support open standards such as full wire protocol compatibility with open source databases, which helps our databases seamlessly integrate with other open source compatible databases hosted in other clouds and on-premises environments. Applications can communicate with AWS open source compatible databases without any modifications. We also offer integration with open source frameworks such as ODBC, JDBC, LangChain and LlamaIndex.
AWS databases are integrated with various AWS services that support multicloud environments. To illustrate, Amazon Elastic Kubernetes Service (EKS) is integrated with Amazon Aurora, Amazon DynamoDB, and Amazon ElastiCache, and more. You can integrate these EKS clusters with other Kubernetes services in a multicloud environment. You can use AWS IAM Roles Anywhere to grant workloads running outside of AWS, including other cloud providers, temporary access to AWS resources using IAM roles and policies. In addition, you can use AWS DMS to move data across clouds.
The ease of interoperability and portability provide you flexibility to deploy your workloads across various environments based on your requirements and constraints.
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Database services
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Database type
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Examples
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AWS service
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Relational
Relational databases store data with predefined schemas and relationships between them. These databases are designed to support ACID transactions, and maintain referential integrity and strong data consistency.
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Traditional applications, enterprise resource planning (ERP), customer relationship management (CRM), ecommerce, generative AI use cases (such as chatbots with Retrieval Augmented Generation, similarity search, recommendation systems, and more) |
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Key-value
Key-value databases are optimized for common access patterns, typically to store and retrieve large volumes of data. These databases deliver quick response times, even in extreme volumes of concurrent requests.
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High-traffic web applications, ecommerce systems, gaming applications, generative AI use cases (such as similarity search using DynamoDB zero-ETL integration with Amazon OpenSearch Service) |
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In-memory
In-memory databases are used for applications that require real-time access to data. By storing data directly in memory, these databases deliver microsecond latency to applications for whom millisecond latency is not enough.
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Caching, session management, gaming leaderboards, geospatial applications, generative AI use cases (such as chatbots with Retrieval Augmented Generation, semantic caching, recommendation systems, fraud detection, and more) |
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Document
A document database is designed to store semistructured data as JSON-like documents. These databases help developers build and update applications quickly.
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Content management, catalogs, user profiles, generative AI use cases (such as chatbots with Retrieval Augmented Generation, similarity search, recommendation systems, and more) |
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Graph
Graph databases are for applications that need to navigate and query millions of relationships between highly connected graph datasets with millisecond latency at large scale.
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Fraud detection, social networking, recommendation engines, generative AI use cases (such as GraphRAG, enhanced fraud detection, discovery of new answers, and more) |
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Wide column
A wide column store is a type of NoSQL database. It uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table.
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High-scale industrial apps for equipment maintenance, fleet management, and route optimization |
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Time series
Time-series databases efficiently collect, synthesize, and derive insights from data that changes over time and with queries spanning time intervals.
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Internet of Things (IoT) applications, DevOps, industrial telemetry |
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