With the plethora of AWS cloud database solutions to choose from, it’s easy to get lost when finding what you need. It’s crucial for an enterprise to select the right kind of database for their workloads, as it in part determines how they will manage as well as monitor and scale with their data.
This guide may help. Below is a summary description of each database service to provide an understanding of which solutions are most applicable to your enterprise’s requirements.
Amazon Relational Database Service (RDS)
Amazon’s initial database service, Amazon RDS handles tasks for cloud-based relational databases. RDS supports other database services such as Amazon Aurora, Oracle Database, SQL Server, MariaDB, PostgreSQL, and MySQL. It can manage and scale database storage, and other tasks such as migration and patching. Instances come with automated backup, recovery to a fixed point in time, and the ability for automatic failover across different availability zones.
Another managed database from Amazon, Aurora can self-heal and scale storage from 10GB to up to 64TB. Aurora Replicas can be provisioned for rapid failover, higher availability, and larger storage. Aurora can also be made as a serverless database that scales automatically and available on demand. Compatible with MySQL and PostgreSQL.
A fully-managed caching service, ElastiCache allows you to create an in-memory cache in the cloud. While built on Redis, it also has a version that is Memcached-compatible. You may deploy, scale, and monitor your cloud in-memory cache for applications, even those that require speeds in mere milliseconds.
Timestream gathers, stores, organizes, and processes time-series data. Once arranged by time intervals, it can give queries that adapt as your data grows. Perfect for keeping and analyzing log data and IoT apps.
This service is a fully-managed SQL database known for its low latency and high scalability. Every DynamoDB table is provided unlimited storage space and throughput, making the service useful for serverless web applications and data store for microservices.
A self-healing storage system that can automatically scale up to 64 TB per cluster, DocumentDB makes it easier to configure, manage and scale MongoDB clusters. MongoDB users can maintain their use of the same tools and resources, as DocumentDB still employs the Apache 2.0 open source MongoDB API, and is thus still compatible with them.
Amazon Neptune is a graph database, allowing users to create and manage scalable graph databases in the cloud. Graph databases are NoSQL databases where users can store, map, and query relationships among data sets. It is secured in an Amazon Virtual Private Cloud, and provides automated backups to Amazon S3.
Amazon Quantum Ledger Database (QLDB)
QLDB provides a centralized, secure, and unchangeable transaction log that can be used to keep track of financial transactions, making it an effective alternative to blockchain. Its permanent data history records every change made.
Amazon Redshift is all about data warehousing. Through machine learning and parallel queries, it can analyze and give insights on petabytes of data at a time. Warehouse management tasks are mostly automated, including provisioning, monitoring, and setup.
AWS Database Migration Service (DMS)
Need to move databases between your onsite infrastructure and the AWS cloud? Should the original database remain available throughout the migration? The AWS DMS tool can do all these things. Migrations can be either homogenous (compatible source and target database engines) or heterogeneous (varying engines). Supported databases are SQL, MySQL, Oracle, and PostgreSQL.
Marc is a senior consultant and co-founder at copebit. He has been working in IT for more than 22 years in various positions. He is a certified AWS Solution Architect (Associate) and has a holistic AWS knowledge.