Amazon Timestream is a fast, scalable, and serverless time series database service for IoT and operational applications that makes it easy to 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. Amazon Timestream saves you time and cost in managing the lifecycle of time series data, and its purpose-built query engine lets you access and analyze recent and historical data together with a single query. Amazon Timestream has built-in time series analytics functions, helping you identify trends and patterns in near real-time. Amazon Timestream is serverless and automatically scales up or down to adjust capacity and performance, so you don’t need to manage the underlying infrastructure, freeing you to focus on building your applications.

Serverless auto-scaling architecture

Amazon Timestream 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. With Amazon Timestream, you don’t need to manage infrastructure or provision capacity. Data ingest and query auto-scale based on your workload.

Data storage tiering

Amazon 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. With Amazon Timestream, you don’t need to configure, monitor, and manage a complex data archival process. 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.

Purpose-built adaptive query engine

With Amazon Timestream, you don’t need to use disparate tools for data access. Amazon Timestream’s purpose-built 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.

Built-in time series analytics

Amazon Timestream supports 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.

Always-encrypted data

All data in Amazon Timestream is automatically encrypted, so you don’t need to manually encrypt data at rest or in transit. Amazon Timestream also enables you to specify an AWS KMS customer managed key (CMK) for encrypting data in the magnetic store.

Amazon Timestream integrates with commonly used services for data collection, visualization, and machine learning. You can send data to Amazon Timestream using AWS IoT Core, Amazon Kinesis, Amazon MSK, and open source Telegraf. You can visualize data using Amazon QuickSight, Grafana, and business intelligence tools through JDBC. You can also use Amazon SageMaker with Amazon Timestream for machine learning.

Performant and cost-effective time-series analytics

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.

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