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.

This post was written by Mercy Kibet, a full-stack developer with a knack for learning and writing about new and intriguing tech stacks. In today’s digital world, software applications are becoming increasingly complex and distributed, making it more challenging than ever to diagnose and troubleshoot issues when they arise.

In this article, we will be exploring C# date classes and how to leverage them to handle and manipulate date data in our applications. We will see the different types of date objects that C# handles and the formats that can be represented, and we will learn how to cleanly process date information from users. Let’s jump right in.

Python is a highly versatile language. From software engineering to machine learning and data analysis, it’s everywhere. As a multipurpose scripting and programming language, it’s often utilized for manipulating and working with data. So, when you’re working with Python, whether you’re analyzing data or writing scripts, you’re likely to encounter dates and time stamps.

This article was originally published in The New Stack and is reposted here with permission. It’s tempting to stuff time series data into the familiar Postgres or MySQL database, but that’s a bad idea for many reasons. To the uninitiated or unfamiliar, time series data exhibits similar characteristics to relational data, but the two data types have some critical differences.