Amazon Timestream features
Performance and scalability
Amazon Timestream is a serverless analytics database for ingesting more than tens of gigabytes of timeseries data per minute and running SQL queries on terabytes of timeseries data in seconds. It is ideal for uses cases such as security analytics and monitoring the quality of streaming video. As X is serverless, it automatically scales up or down to adjust capacity and performance, so you don’t need to manage the underlying infrastructure or provision capacity. It features a fully decoupled architecture where data ingestion, storage, and query can scale independently, allowing it to offer virtually infinite scale for an application’s needs.
Timestream simplifies your data lifecycle management with a memory store for recent data and a magnetic store for historical data. The memory store is optimized for fast point-in-time queries, and the magnetic store is optimized for fast analytic queries. You can simply configure data retention policies to automatically move data from the memory store to the magnetic store and to delete it from the magnetic store when it reaches a certain age.
Timestream’s adaptive query engine allows you to access data across storage tiers using a single SQL statement. It transparently accesses and combines data across storage tiers without requiring you to specify the data location. With X, you can store and analyze trillions of events per day up to 1,000 times faster and at as little as 1/10th the cost of relational databases. Its query engine lets you access and analyze recent and historical data together with a single query. It has built-in time series analytics functions, helping you identify trends and patterns in near real time.
All data in Amazon Timestream is automatically encrypted by default, so you don’t need to manually encrypt data at rest or in transit. With native integrations for AWS IAM and AWS KMS services, you can securely manage access to your resources and data, including specifying an AWS KMS customer managed key (CMK) for encrypting data in the magnetic store.
Amazon Timestream also enables you to protect your time series data, through integration with AWS Backup, to help you meet your compliance and business continuity needs. Using this fully managed functionality, you can create immutable backups, automate backup lifecycle management, and copy those backups across AWS accounts and Regions. In addition, you can schedule periodic backups of your data to meet your regulatory needs. The first backup of your table is a full backup, and subsequent backups of the same table are incremental, only copying the changes since the last backup, making it flexible and cost-effective to protect your data.
You can create different backup plans for the Timestream tables in your account, enabling you to protect each resource based on your specific regulatory and business continuity needs. You can also set retention policies that will automatically retain, expire, and transition backups to cold storage, minimizing backup storage costs. Additionally, you can restore the entire table to a database with a few clicks, simplifying data recovery.
Integrations with AWS services
Amazon Timestream supports high performance and cost-effective time series analytics, and defines time series as a native data type. It supports advanced aggregates, window functions, and complex data types such as arrays and rows. Amazon Timestream’s scheduled queries offer a fully managed, serverless, and scalable solution for calculating and storing aggregates, rollups, and other real-time analytics used to power frequently accessed operational dashboards, business reports, applications, and device-monitoring systems.
With scheduled queries, you simply define the queries that calculate aggregates, rollups, and other real-time analytics on your incoming data. Amazon Timestream periodically and automatically runs these queries and reliably writes the results into a configurable destination table. You can then point your dashboards, reports, applications, and monitoring systems to simply query the destination tables instead of querying the considerably larger source tables containing the incoming time-series data. This leads to increased performance while reducing cost by an order of magnitude.
The destination tables contain much less data than the source tables, thereby offering faster and less expensive data access and storage. Given that destination tables contain much less data than source tables, you can store data in the destination tables for a much longer duration at a fraction of the storage cost of the source table. You can also choose to reduce the data retention period of your source tables and further optimize your spend. Scheduled queries can therefore make time-series analytics faster, more cost effective, and more accessible to many more customers, so you can continue to make better data-driven business decisions.