Operations | Monitoring | ITSM | DevOps | Cloud

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.

Kubernetes or K8s is an open-source production-grade container orchestration system for automating, scaling, and managing containerized applications. A container is a lightweight, standalone, executable ready-to-run software package that contains everything needed to run an application. It includes the runtime, code, libraries, systems tools, and default values for any essential settings.

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!).

The way we handle massive volumes of data from multiple sources is about to change fundamentally. The traditional data processing systems don’t always fit into our budget (unless you have some pretty deep pockets). Our wallets constantly need to expand to keep up with the changing data veracity and volume, which isn’t always feasible. Yet we keep doing it because data is a commodity.

Data is both a challenge and an asset for IT professionals, who rely on IT Operations Analytics (ITOA) to guide them towards operational excellence, system reliability, and swift incident resolution. So whether you’re seeking clarity on understanding what ITOA is and its connection to related technologies, are contemplating how to use it within your organization, or are curious about its enhanced efficiency and cost savings benefits, we’ve got you covered.

Welcome to the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language. This blog post (Chapter 1) is focused on.

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.