Amazon Web Services has released Timestream, a new time series database service for IoT that is designed to make 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.

Timestream has been designed to give users the means to manage time series data easily, quickly and cost effectively. Amazon says that many organizations have useful data buried in industrial equipment, website clickstream logs, and many other sources, but it's too hard to get at and use. Timestream has been purpose-built to manage the scale and complexity of time series data in the cloud.

Amazon Timestream is scalable and serverless. It keeps recent data in memory and moves historical data to a cost optimized storage tier based upon user defined policies. The database has a purpose-built query engine for analyzing recent and historical data together, without needing to specify explicitly in the query whether the data resides in the in-memory or cost-optimized tier.

Data is automatically replicated across multiple availability zones (AZ) in the same AWS region. New data is written to the memory store, where data is replicated across three AZs. Data replication is quorum based, and data in the memory store is also continuously backed up to Amazon Simple Storage Service (S3).

Users can analyze time series data using SQL, with built-in time series functions for smoothing, approximation, and interpolation. Amazon Timestream also supports advanced aggregates, window functions, and complex data types such as arrays and rows.

Timestream will ingest data directly from AWS IoT Core, Amazon Kinesis Data Analytics for Apache Flink, and Amazon MSK, and comes with tools to use Timestream with open source platforms such as Apache Kafka, Telegraf, Prometheus, and Grafana.

More Information

Timestream website

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