AWS Cloud Databases
Build use case-driven, highly scalable, distributed applications suited to your specific needs. AWS offers 15+ purpose-built engines to support diverse data models, including relational, key-value, document, in-memory, graph, time series, wide column, and ledger databases.
Free your teams from time-consuming database tasks like server provisioning, patching, and backups. AWS fully managed database services provide continuous monitoring, self-healing storage, and automated scaling to help you focus on application development.
Start small and scale as your applications grow with relational databases that are 3-5X faster than popular alternatives, or non-relational databases that give you microsecond to sub-millisecond latency. Match your storage and compute needs easily, often with no downtime.
Support multi-region, multi-primary replication, and provide full data oversight with multiple levels of security, including network isolation and end-to-end encryption. AWS databases deliver the high availability, reliability, and security you need for business-critical, enterprise workloads.
Database services
Relational Database
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.
Amazon Aurora
MySQL and PostgreSQL-compatible relational database built for the cloud. Performance and availability of commercial-grade databases at 1/10th the cost
Amazon Relational Database Service (RDS)
Set up, operate, and scale a relational database in the cloud with just a few clicks
Amazon Redshift
Analyze all of your data with the fastest and most widely used cloud data warehouse
Key-value Database
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.
Amazon DynamoDB
Get a fast, flexible, and serverless NoSQL database for any scale, to support key-value and document workloads
In-memory Database
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.
Document Database
A document database is designed to store semistructured data as JSON-like documents. These databases help developers build and update applications quickly.
Amazon DocumentDB (with MongoDB compatibility)
Scale JSON workloads with ease using an enterprise-ready document database service compatible with MongoDB.
Wide Column Database
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.
Amazon Keyspaces
Run your Apache Cassandra workloads on a scalable, highly available, and managed wide column database service.
Graph Database
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.
Amazon Neptune
Build applications that work with highly connected datasets using a fast, reliable graph database service.
Time Series Database
Time-series databases efficiently collect, synthesize, and derive insights from data that changes over time and with queries spanning time intervals.
Amazon Timestream
Store and analyze trillions of events per day with a fast, scalable, and serverless time series database service.
Ledger Database
Ledger databases provide a centralized and trusted authority to maintain a scalable, immutable, and cryptographically verifiable record of transactions for every application.
Amazon Quantum Ledger Database (QLDB)
Provide transparent, immutable, cryptographically verifiable transaction logs with a fully managed ledger database service.
Database services
| Database Type | Use Cases | AWS Service |
|---|---|---|
| Relational | Traditional applications, ERP, CRM, e-commerce | Amazon Aurora | Amazon RDS | Amazon Redshift |
| Key-value | High-traffic web apps, e-commerce systems, gaming applications | Amazon DynamoDB |
| In-memory | Caching, session management, gaming leaderboards, geospatial applications | Amazon ElastiCache | Amazon MemoryDB for Redis |
| Document | Content management, catalogs, user profiles | Amazon DocumentDB (with MongoDB compatibility) |
| Wide-column | High scale industrial apps for equipment maintenance, fleet management, and route optimization | Amazon Keyspaces |
| Graph | Fraud detection, social networking, recommendation engines | Amazon Neptune |
| Time series | IoT applications, DevOps, industrial telemetry |
Amazon Timestream |
| Ledger | Systems of record, supply chain, registrations, banking transactions | Amazon Quantum Ledger Database (QLDB) |
Use cases
Move to managed databases
Build modern apps with purpose-built databases
Choose the database service best fit for the job to help you optimize scale, performance, and costs when designing applications. See how purpose-built databases match up with modern microservices architectures.
Break free from legacy databases
Stop working around proprietary standards, punitive pricing terms, and frequent audits. Embrace open-source compatible cloud databases with commercial grade performance, availability, and scale at a fraction of the cost.
Samsung migrated 1.1 billion users across three continents from Oracle to Amazon Aurora.
"The scalability of Amazon Aurora is the best benefit—especially if we focus on the cost. Samsung reduced monthly database costs by 44%."
- Salva Jung, Principal Architect and Engineering Manager
Case studies
Experian uses Amazon DynamoDB and Amazon Aurora’s high availability to achieve 100 percent operation uptime.
A+E Networks uses serverless AWS databases to facilitate expansion by creating microservices-driven cloud-native applications.
Pokémon migrated to AWS purpose-built databases to save tens of thousands of dollars per month.
Cathay Pacific modernized its passenger revenue optimization system on AWS and increased performance by 20 percent.