The InfluxDB platform has evolved a lot over the past decade. But with every innovation we’ve added to the platform, the focus behind our efforts has remained the same: Build cool stuff for people who build cool stuff. What we mean by this is we want to make it incredibly easy for users to build valuable applications with their time series data. We do that by offering a wide range of tools, features, and resources that meet builders on their terms.

We love to write and ship code to help developers bring their ideas and projects to life. That’s why we’re constantly working on improving our product in sync with developer needs to ensure their happiness and accelerate Time To Awesome. This month is very special. We now have a new engine that significantly increases the “horsepower and torque” for InfluxDB.

Native MQTT eliminates the need to write custom code, orchestrate additional technology layers or incorporate additional hosting services. MQTT is a powerhouse within the Internet of Things (IoT) space. Its pub/sub model and lack of defined payload structure make it infinitely adaptable to the needs of modern sensors, devices and systems. IoT data is also time-series data.

The latest release of Elastic Enterprise Search introduces a suite of new features and capabilities for building world-class search experiences for your mission-critical applications, websites, online stores, or anything in-between: With this release, building search experiences for your ecommerce retail site, enhancing your employees’ ability to access relevant HR documentation, or building a custom application to quickly draw analytic insights can draw on the power of using the trained model of

Data observability is a relatively new discipline in the fields of data engineering and data management. While many are familiar with the longstanding concepts of observability and monitoring in enterprise IT networks and infrastructure, data observability has only really come into the spotlight in the last two years. However, it has managed to turn a lot of heads in that short time.

This article was originally published in The New Stack and is reposted here with permission. Working with geo-temporal data can be difficult. In addition to the challenges often associated with time-series analysis, like large volumes of data that you want real-time access to, working with latitude and longitude often involves trigonometry because you have to account for the curvature of the Earth. That’s computationally expensive. It can drive costs up and slow down programs.