The stakes are high in Aerospace manufacturing and operations. Aerospace systems are highly complex and require extremely precise engineering—every part of an aircraft or spacecraft must work together flawlessly, and error tolerance is minuscule. Ensuring that all components work perfectly under various conditions (pressure, temperature, vibration) is vital. The cost of building and operating aerospace systems is enormous.

In a world driven by data, efficient time series data management is a growing concern. APIs play a significant role in automating tasks, especially in cloud-based environments. Go, with its high performance and concurrency, is quickly becoming one of the standard languages for writing cloud infrastructure and utilities for managing streams of data.

This blog was originally published on Apache DataFusion Project News & Blog I am extremely excited to announce that Apache DataFusion 43.0.0 is the fastest engine for querying Apache Parquet files in ClickBench. It is faster than both DuckDB and chDB/Clickhouse using the same hardware. It also marks the first time a Rust based engine holds the top spot, which has previously been held by traditional C/C++ based engines.

In this blog post, we’ll explore how to build a data pipeline using Kafka, Faust, and InfluxDB to effectively ingest, transform, and store data. We’ll start with an overview of Kafka, a high-performance messaging platform, and Faust, a Python library designed for stream processing, now maintained by the community as Faust-streaming.

If you’re looking to dive into the world of IoT data collection, you’ve probably come across MQTT—a lightweight messaging protocol designed for efficiently transmitting data between devices.

In a previous post, we described the technique that makes the ”most recent values” queries hundreds of times faster and has benefited many of our customers. The idea behind this technique is to progressively evaluate time-organized files until we reach the most recent values.

In the first blog in this series, Setting Up InfluxDB and Visualizing Data: Part 1, we built a data collection and visualization platform for time series data using InfluxDB Cloud Serverless. Inspired by the CSTR with PID controllers use case, the project showcased how to ingest real-time data and visualize it using InfluxDB and Grafana. This follow-up post focuses on InfluxDB’s 3.0 architecture, giving an in-depth look at the platform’s inner workings.

We have added a new function, tz, to InfluxDB 3.0 SQL to improve the ergonomics of working with timezones. This blog post will provide more details on how to use this function and when it makes sense to use it instead of the existing SQL timezone functions.

We are excited to announce the launch of User Groups, a major update that facilitates enhanced security through access control in InfluxDB Cloud Dedicated. This new feature allows for more granular access management by limiting limited access accounts. Giving customers more access control helps them implement PoLP (“Principle of Least Privilege”) for improved security.

The demand for LLM is rapidly increasing—it’s estimated that there will be 750 million apps using LLMs by 2025. As a result, the need for LLM observability and monitoring tools is also rising. In this blog, we’ll dive into what LLM monitoring and observability are, why they’re both crucial and how we can track various metrics to ensure our model isn’t just working but thriving.