Operations | Monitoring | ITSM | DevOps | Cloud

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.

Imagine being able to foresee future trends, anticipate customer behaviour, optimize your operations, and take actions that are not just reactive — they shape the future of the market. In the world of data-driven decision-making, we're able to do all that by paying attention to the information we analyze from predictive and prescriptive analytics. A large and growing field, data analytics is often broken into four categories — of which predictive and prescriptive are two!

The cloud’s elasticity—the ability to scale resources up and down in response to changes in demand—as well as variable cost structures offer significant advantages, enabling enterprises to move from rigid capex models to elastic opex models where they pay for what they provision, with engineers in control and focused on innovation, becoming true business accelerators.