AWS offers 14 databases that support diverse data models and include the following types of databases: relational, key-value, document, in-memory, graph, time series, and ledger databases. In this article, we’ll cover the following AWS databases:

Amazon Timestream
Amazon RDS on VMware
Amazon Quantum Ledger Database

To dive deeper into Amazon databases, check out Cloud Academy’s Working with AWS Databases Learning Path. This learning path introduces you to the different AWS database services and some of the features relating to AWS database types.

Amazon Timestream
Data has become more and more valuable to organizations. The information that can be extracted from it has brought rise to the use of data lakes which provide a deeper insight into all of your data, enabling a greater business strategy. As a part of this, the Internet of Things (IoT) industry has also exponentially grown, along with the data that these systems provide. Much of this data can be classified as time-series data — essentially data that assesses how events change over time.  To help gather, maintain, and query this data, AWS has developed a new database called Amazon Timestream. 
Amazon Timestream is a serverless database offering that is specifically focused on time-series data. Much like other AWS database services, Amazon Timestream is fully managed and takes much of the administration and maintenance out of your hands, giving you time to work with and manage your data. As your data grows, so does your storage. It’s fully scalable, ensuring you never run out of space. Other features include its ability to automatically configure retention, tiering, and data compression. When these features are combined, all of these benefits help to provide a service at a reduced cost.
One of its key features is that it can store and process trillions of events every single day — with huge cost savings against typical relational databases —

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