Amazon Timestream Documentation

Documentation

Amazon Timestream is a fast, scalable, and serverless time series database service for IoT and operational applications that is designed to help you to store and analyze trillions of events per day up to 1,000 times faster than relational databases. Amazon Timestream can help save you time and cost in managing the lifecycle of time series data, and its 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 scales up or down to adjust capacity and performance, so you don’t need to manage the underlying infrastructure.

Serverless scaling architecture

Amazon Timestream features a decoupled architecture where data ingestion, storage, and query can scale independently, allowing it to scale for an application’s needs. With Amazon Timestream, you don’t need to manage infrastructure or provision capacity. Data ingest and query scale based on your workload.

Data storage tiering

Amazon Timestream helps simplify your data lifecycle management with a memory store for recent data and a magnetic store for historical data. The memory store is designed for fast point in time queries, and the magnetic store is designed 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 move data from the memory store to the magnetic store, and to delete it from the magnetic store when it reaches a certain age.

Adaptive query engine

Amazon Timestream’s adaptive query engine allows you to access data across storage tiers using a single SQL statement. It is designed to transparently access 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.

Encryption

All data in Amazon Timestream is encrypted. Amazon Timestream also enables you to specify an AWS KMS customer managed key (CMK) for encrypting data in the magnetic store.

Integrates with popular data collection, visualization, and machine learning tools

Amazon Timestream integrates with a selection of 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.

Time-series analytics

Amazon Timestream’s scheduled queries are designed 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 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 writes the results into a configurable destination table. You can then point your dashboards, reports, applications, and monitoring systems to query the destination tables instead of querying the source tables containing the incoming time-series data. The destination tables contain less data than the source tables, which can result in faster and less expensive data access and storage.

Since destination tables contain less data than source tables, you can store data in the destination tables for a longer duration and for less storage cost than the source table. You can also choose to reduce the data retention period of your source tables to optimize your spend.

Data backup

Amazon Timestream enables you to protect your time series data, through integration with AWS Backup. Using this 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. 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.

You can create different backup plans for the Timestream tables in your account, 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. Additionally, you can quickly restore the entire table to a database.

Additional Information

For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see https://docs.thinkwithwp.com/index.html. This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at http://thinkwithwp.com/agreement, or other agreement between you and AWS governing your use of AWS’s services.