Words to go: AWS databases

Organizations must choose the proper database for their workloads as part of a solid IT strategy. This environment will determine the way they manage, monitor, scale and interact with data over time.

AWS offers a wide range of databases, but it can be difficult to understand how those services differ. Follow this rundown of AWS databases -- with details on deployment, management methods, availability and scalability -- to better understand which options best suit your organization's needs.

Amazon RDS: AWS' original database offering, Amazon Relational Database Service (RDS) manages and scales relational database instances in the cloud. It handles tasks such as database storage, migration and patching. RDS instances are created with automatic backup, point-in-time recovery and the option for automatic failover across availability zones. Amazon RDS supports Amazon Aurora, Oracle Database, SQL Server, MariaDB, PostgreSQL and MySQL.

Amazon Aurora: This managed relational database has MySQL and PostgreSQL compatibility. Amazon Aurora is self-healing and scales storage from 10 GB to 64 TB. Developers can create Amazon Aurora Replicas for faster failover, greater availability and increased storage. Amazon Aurora is also available in a serverless version, with auto scaling and on-demand capabilities.

Amazon ElastiCache: ElastiCache is an in-memory data store built on Redis. There's also a version compatible with Memcached. The service deploys, scales and continuously monitors in-memory cache for real-time applications that require sub-millisecond speeds.

Amazon Timestream: One of several serverless AWS databases, Timestream collects, stores and processes time-series data. It organizes data by time intervals and presents queries that adapt to data growth over time. You can use this database to store and analyze log data and IoT applications.

Amazon DynamoDB: This managed NoSQL document database provides low latency and high scalability. DynamoDB replicates data across three availability zones and is also available with cross-region replication. Users have unlimited storage and throughput for each DynamoDB table. Common use cases for this database include serverless web applications and microservices data stores.

Amazon DocumentDB: Also a document database, DocumentDB was designed to simplify the setup, management and scaling of MongoDB clusters. DocumentDB implements the Apache 2.0 open source MongoDB API, so MongoDB clients can continue to use their current tools and resources in DocumentDB. The AWS database service uses a distributed, self-healing storage system that auto scales up to 64 TB per database cluster, according to AWS.

Amazon Neptune: A graph database service, Amazon Neptune manages NoSQL databases used to store, map and identify relationships between data sets. Neptune writes queries to connect data points with low latency. It also runs on an Amazon Virtual Private Cloud and handles automatic backups to Amazon S3.

Amazon QLDB: Amazon Quantum Ledger Database (QLDB) is a managed service for ledger-based applications and operates as an alternative to blockchain platforms. It presents a centralized, immutable transaction log, typically used to track financial activity. Amazon QLDB provides a permanently stored data history and tracks every modification made over time.

Amazon RedShift: This long-standing AWS service provides petabyte-scale data warehousing. Redshift uses machine learning and parallel query execution to present fast and scalable data insights. It also automates a number of warehouse management tasks such as provisioning, monitoring and configuration.

AWS DMS: AWS Database Migration Service (DMS) moves databases to and from the AWS cloud and keeps the source database operational until the migration is complete. These migrations can be homogenous, where the source and target database engines are compatible, or heterogeneous, where the two engines differ. The service supports Oracle, SQL Server, MySQL and PostgreSQL in the cloud and on premises.