Industrial IoT (IIoT) machines and sensors generate valuable time series data. It’s impossible to derive the insights necessary to inform decisions as a company to produce or operate more efficiently without sending operational technology (OT) data to informational technology (IT) systems.

InfluxDB v3 allows users to write data at a rate of 4.3 million points per second. However, an incredibly fast ingest rate like this is meaningless without the ability to query that data. Apache DataFusion is an “extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.” It enables 5–25x faster query responses across a broad range of query types compared to previous versions of InfluxDB that didn’t use the Apache ecosystem.

This article coins the term “FDAP stack”, explains why we used it to build InfluxDB 3.0, and argues that it will enable and power a generation of analytics applications in the same way that the LAMP stack enabled and powered a generation of interactive websites (by the way we are hiring!).

Imagine, if you will, having hundreds of devices that you need to monitor. All these devices generate data at sub-second intervals, and you need all that high fidelity data for historical analysis to feed machine learning models. Storing all that data can get really expensive, really fast. When that happens, you must decide what’s more important: keeping all your data or sacrificing insights and analysis. It may not be a big stretch of the imagination for many readers.

With our plans for InfluxDB 3.0 OSS laid out, both myself and the rest of the DevRel team have been actively searching for ecosystem platforms that would be logical integrations for the future of InfluxDB. One of these platforms is Quix! Quix is a comprehensive solution tailored for crafting, launching, and overseeing event streaming applications using Python. If you’re looking to sift through time series or event data in real-time for instant decision-making, Quix is your go-to.

With the smart home revolution in full swing, choosing the proper hardware for platforms like Home Assistant can be overwhelming. Whether you’re new to home automation or a seasoned pro, the hardware you select can make or break your experience. But fear not! This comprehensive guide will demystify the requirements, delve into the various options, and help you make an informed decision. From the compact Raspberry Pi to the powerful Intel NUC, we’ve got you covered.

In the field of big data analytics, stream processing has emerged as a crucial paradigm, reshaping how businesses interact with data. But what is stream processing and why are more businesses using it?

This article was originally published on The New Stack and is reposted here with permission. By taking advantage of monitoring data, companies can ensure their infrastructure is performing optimally while reducing costs. While building new features and launching new products is fun, none of it matters if your software isn’t reliable. One key part of making sure your apps run smoothly is having robust infrastructure monitoring in place.

Time series data is foundational in almost all applications and services. Even if time series isn’t the focus, like in an IoT sensor data centered application, it appears in monitoring data as metrics, logs, and traces. Because of time series data’s unique characteristics, it’s best served in a time series database. InfluxDB is purpose-built to handle the high volume and velocity of time series ingestion, and perform real-time analytics, alerting, and anomaly detection at scale.

This article was originally published on The New Stack and is reposted here with permission. Here is a brief case study that explores the logistics and motivations that would lead a successful company to spend time and resources completely rewriting the core of their flagship product in Rust. Calling a programming language Rust almost seems like a misnomer. Rust is the brittle byproduct of corrosion — not something that would typically inspire confidence.

A few weeks ago, we published some benchmarking that showed performance gains in InfluxDB 3.0 that are orders of magnitude better than previous versions of InfluxDB – and by extension, other databases as well. There are two key factors that influence these gains: 1. Data ingest, and 2. Data compression. This begs the question, just how did we achieve such drastic improvements in our core database? This post sets out to explain how we accomplished these improvements for anyone interested.