The concept of observability centers around collecting data from all parts of the system to provide a unified view of the software at large. Fault tolerance, no single point of failure and redundancy are prominent design principles in modern software systems. But that doesn’t mean errors, degradation, bugs or even the occasional catastrophe don’t happen.

The panel discussion “From Machine Data to Business Insights, Building the Foundations of Industrial Analytics” discussed modern methods and benefits of deriving insights from machine data. InfluxDB Developer Advocate Jay Clifford explained the trend now is to “allow the builders to bring the Lego blocks and build them together how they see fit.

This post covers how to get started with Home Assistant and Grafana, including setting up InfluxDB and Grafana with Docker, configuring InfluxDB to receive data from Home Assistant, and creating a Grafana dashboard to visualize your data. It provides a comprehensive guide for real-time monitoring and analysis of Home Assistant data. In this tutorial, you’ll learn how to integrate Grafana with Home Assistant using InfluxDB.

This post was written by Siddhant Varma. Scroll down for the author’s bio. Observability is an essential aspect of a healthy software architecture and a highly performant system. It enables developers and engineers to understand and dive deeper into how their application behaves. This in turn helps them monitor it effectively.

InfluxDB 3.0 has 10x better storage compression and performance, supports unlimited cardinality data, and delivers lightning-fast SQL queries compared to previous versions. These gains are the result of our new database engine built on top of Apache Arrow. Apache Arrow processes huge amounts of columnar data and provides a wide set of tools to operate effectively on that data.

Exponential smoothing is a time series forecasting method that uses an exponentially weighted average of past observations to predict future values. This method assigns more weight to recent observations and less to older observations, allowing the forecast to adapt to changing trends in the data. The resulting forecast is a smoothed version of the original time series less affected by random fluctuations or noise in the data.