In addition to the storage options discussed in the previous chapters, AWS offers a broad range of databases purposely built for your specific application use cases. You can also set up your own database platform on the Amazon Elastic Compute Cloud (Amazon EC2). You can easily migrate your existing databases with the AWS Database Migration Service (AWS DMS) in a cost-effective manner.
AWS Cloud offerings include the following databases:
Managed relational databases—For transactional applications Nonrelational databases —For internet-scale applications Data warehouse databases —For analytics In-memory data store databases —For caching and real-time workloads Time-series databases —For efficiently collecting, synthesizing, and deriving insights from time-series data Ledger databases —For when you need a centralized, trusted authority to maintain a scalable, complete, and cryptographically verifiable record of transactions Graph databases —For building applications with highly connected data
Depending on your use case, you can choose from an AWS database that closely aligns with your needs. The table describes each database service that AWS offers and indicates the database type.
| Product | Type | Description |
|---|---|---|
| Amazon Aurora | Relational database | A MySQL- and PostgreSQL-compatible relational database built for the cloud that combines the performance and availability of traditional enterprise databases with the simplicity and costeffectiveness of open source databases. |
| Amazon Relational Database Service (Amazon RDS) | Relational database | A managed relational database for MySQL, PostgreSQL, Oracle, SQL Server, and MariaDB. Easy to set up, operate, and scale a relational database in the cloud quickly. |
| Amazon Redshift | Data warehouse | A fast, fully managed, petabyte-scale data warehouse at one-tenth the cost of traditional solutions. Simple and costeffective solution to analyze data by using standard SQL and your existing business intelligence (BI) tools. |
| Amazon ElastiCache | In-memory data store | To deploy, operate, and scale an in-memory data store based on Memcached or Redis in the cloud. |
| Amazon Neptune | Graph database | A fast, reliable, fully managed graph database to store and manage highly connected datasets. |
| Amazon Document DB (with MongoDB compatibility) | Nonrelational database | A fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads. |
| Amazon Timestream | Time series database | A fast, scalable, fully managed time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day at one-tenth the cost of relational databases. |
| Amazon Quantum Ledger Database (Amazon QLDB) | Ledger database | A fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log owned by a central trusted authority. |
| AWS Database Migration Service (AWS DMS) | Database migration | Help migrate your databases to AWS easily and inexpensively with minimal downtime. |
Now that you know the purpose of these database services and what they can do, review the type of applications that can be used for each AWS database service.
| Applications | Product |
|---|---|
| Transactional applications, such as ERP, CRM, and ecommerce to log transactions and store structured data. | Aurora or Amazon RDS |
| Internet-scale applications, such as hospitality, dating, and ride sharing, to serve content and store structured and unstructured data. | DynamoDB or Amazon DocumentDB |
| Analytic applications for operational reporting and querying terabyte- to exabyte-scale data. | Amazon Redshift |
| Real-time application use cases that require submillisecond latency such as gaming leaderboards, chat, messaging, streaming, and Internet of Things (IoT). | ElastiCache |
| Applications with use cases that require navigation of highly connected data such as social news feeds, recommendations, and fraud detection. | Neptune |
| Applications that collect data at millions of inserts per second in a time-series fashion, for example clickstream data and IoT devices. | Timestream |
| Applications that require an accurate history of their application data; for example, tracking the history of credits and debits in banking transactions or verifying the audit trails created in relational databases. | Amazon QLDB |