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Amazon Timestream is a fast, scalable, fully managed, purpose-built time series database that makes it easy to store and analyze trillions of time series data points per day. Amazon Timestream saves you time and cost in managing the lifecycle of time series data by keeping recent data in memory and moving historical data to a cost optimized storage tier based upon user defined policies. Amazon Timestream’s purpose-built query engine lets you access and analyze recent and historical data together, without having to specify its location. Amazon Timestream has built-in time series analytics functions, helping you identify trends and patterns in your data in near real-time. Timestream is serverless and automatically scales up or down to adjust capacity and performance. Because you don’t need to manage the underlying infrastructure, you can focus on optimizing and building your applications.

Amazon Timestream also 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. For more information on how to use Amazon Timestream see the AWS documentation.

This repository contains sample applications, plugins, notebooks, data connectors, and adapters to help you get started with Amazon Timestream and to enable you to use Amazon Timestream with other tools and services.

Below is a fully functional sample application that shows how to create a database and table, populate the table with ~126K rows of sample data, and run sample queries. This sample application is currently available for the following programming languages:

• Java
• Java v2
• Python
• Go
• Node.js
• .NET

The following are sample data connectors, adapters, and notebooks show how to use Amazon Timestream with and popular tools and services used for data collection, visualization, and machine learning:

• Working with Amazon SageMaker Notebook
• Publishing data with AWS IoT Core
• Output data connector for open source Telegraf
• Apache Flink sample data connector

The following tools can be used to continuously send data into Amazon Timestream:

• Publishing data with Amazon Kinesis
• Writing data using a multi-thread Python DevOps data generator