We announced the general availability (GA) of Azure Time Series Insights (TSI). TSI is a cost-effective and performant service for the analytics, visualization, and storage of time series data. For the last seven months hundreds of customers, including ThyssenKrupp, BMW, Steelcase, TransAlta, Actionpoint, and Mesh Systems, have pushed more than 50 billion events into TSI for use in their production environments. Customers have leveraged TSI to visualize machine learning models in real-time, compare disparate assets, reduce SLA’s for IoT asset validation and deployment, and conduct root cause analysis. Now, customers with large volumes of time series data have a scalable, commercial-grade solution for storing and analyzing data without the headache and expense of tedious resource management.
When we first started working with customers on TSI, there wasn’t a clean way to analyze and visualize time series data at scale. Historically, time series data is stored in traditional databases hosted on premises where they are hard to set-up and manage. While customers can hobble together commercial and open source products today, they are difficult to provision and get pricey and time-consuming quickly. Once customers get these solutions up and running, they struggle to keep up with the increasing size of their IoT data. In fact, we’ve heard multiple times that these types of solutions are “where good data goes to die,” since customers often wind up not generating any meaningful insights from them.