Amazon Timestream

Fast, scalable, fully managed time series database

Amazon Timestream is a fast, scalable, fully managed time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day at 1/10th the cost of relational databases. Driven by the rise of IoT devices, IT systems, and smart industrial machines, time-series data — data that measures how things change over time — is one of the fastest growing data types. Time-series data has specific characteristics such as typically arriving in time order form, data is append-only, and queries are always over a time interval. While relational databases can store this data, they are inefficient at processing this data as they lack optimizations such as storing and retrieving data by time intervals. Timestream is a purpose-built time series database that efficiently stores and processes this data by time intervals. With Timestream, you can easily store and analyze log data for DevOps, sensor data for IoT applications, and industrial telemetry data for equipment maintenance. As your data grows over time, Timestream’s adaptive query processing engine understands its location and format, making your data simpler and faster to analyze. Timestream also automates rollups, retention, tiering, and compression of data, so you can manage your data at the lowest possible cost. Timestream is serverless, so there are no servers to manage. It manages time-consuming tasks such as server provisioning, software patching, setup, configuration, or data retention and tiering, freeing you to focus on building your applications.

Amazon Timestream announcement at AWS re:Invent 2018

Benefits

1,000X FASTER AT 1/10TH THE COST

Timestream gives you the scale and speed to process trillions of events per day, with up to 1,000X faster query performance at 1/10th the cost of relational databases. Unlike relational databases, Timestream organizes data by time intervals, reducing the amount of data that needs to be scanned to answer a query. It executes inserts and queries in separate processing tiers which eliminates resource contention to improve performance.

BUILT-IN ANALYTICS

Quickly prepare and analyze time-series data with built-in analytic functions such as smoothing, approximation, and interpolation. In addition, Timestream’s adaptive query processing engine is optimized for a wide variety of time intervals such as milliseconds, microseconds, and nanoseconds, making it easy for you to analyze time-series data.

SERVERLESS

With Timestream, there are no servers to manage. As your application needs change, Timestream automatically scales up or down to adjust capacity and performance. Timestream takes care of the time-consuming tasks, such as server provisioning, software patching, setup, and configuration, so you can focus on building your applications. You can use Timestream’s data retention policies to automate how data is stored, reducing the cost of managing your data as it grows over time.

How it works

Timestream - How it works

Use cases

DevOps

Timestream is ideal for DevOps applications that collect data at millions of inserts per second and analyze that data in realtime to improve application performance and availability. For example, with Timestream, you can collect and analyze application data for application performance management, network data for network optimization, and server monitoring data to improve the availability of your infrastructure.

Industrial Telemetry

Timestream enables you to easily store and analyze time-series data at scale for industrial equipment maintenance, trade monitoring, fleet management, and route planning and optimization. Timestream’s adaptive query processing engine and data retention policies adjust the query performance and storage capacity to maintain steady, predictable performance at the lowest possible cost as your data grows over time.

IoT Applications

Timestream makes it possible for you to quickly analyze time-series data generated by IoT applications using built-in analytic functions such as such as smoothing, approximation, and interpolation. For example, a smart home device manufacturer can use Timestream to collect motion or temperature data from the device sensors, interpolate to identify the time ranges without motion, and alert consumers to take actions such as turning off the lights or turning down the heat to save energy.

Application Monitoring

Timestream enables you to easily store and analyze clickstream data at scale to understand the customer journey—the user activity across your applications over a period of time. For example, you can use Timestream to store and process the incoming and outgoing web traffic for your applications. Timestream also provides analytic functions to analyze this data and get insights such as path-to-purchase and shopping cart abandonment.