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.

InfluxDB Cloud 3.0 is a versatile time series database built on top of the Apache ecosystem. You can query InfluxDB Cloud with the Apache Arrow Flight SQL interface, which provides SQL support for working with time series data. In this tutorial, we will walk through the process of querying InfluxDB Cloud with Flight SQL, using Java. The Java Flight SQL Client is part of Apache Arrow Flight, a framework for building high-performance data services.

Over the past few years, time series is one of the fastest growing database categories in the world. As more and more organizations realize how critical time series data is to their operations, more database options entered the market. InfluxDB has been the leading time series database for years, and with the release of InfluxDB 3.0, it remains at the vanguard of the time series world.

A look at the main things you need to consider when planning your IoT project with links to tutorials and source code. There’s a lot of stuff written about the Internet of Things (IoT) at a conceptual level that doesn’t really cover anything concrete. If you’ve ever wanted to get started on a real IoT project but didn’t know where to start, you are in the right place.

This article was originally published in The New Stack and is reposted here with permission. They require different approaches for storage and querying, making it a challenge to use a single solution. But InfluxDB is working to consolidate them into one. Time series data has unique characteristics that distinguish it from other types of data. But even within the scope of time series data, there are different types of data that require different workloads.

Here at InfluxData, we recently announced InfluxDB 3.0, which expands the number of use cases that are feasible with InfluxDB. One of the primary benefits of the new storage engine that powers InfluxDB 3.0 is its ability to store traces, metrics, events, and logs in a single database. Each of these types of time series data has unique workloads, which leaves some unanswered questions. For example: Luckily this is where our work within OpenTelemetry comes into play.