Operations | Monitoring | ITSM | DevOps | Cloud

Summary A new async Rust client for InfluxDB. Built for the edge gateways, embedded systems, and high-throughput ingest pipelines where Rust already runs. Table of Contents Time series data shows up wherever the physical world meets software. A satellite constellation streams altitude, power, and thermal telemetry from every spacecraft on every pass. A factory floor running on Industry 4.0 principles instruments every line, every motor, every batch.

Summary Physical AI—machines and industrial systems that sense conditions, reason, and act in the real world—needs two things from operational data: detailed history for training, and real-time telemetry for inference. Traditional data historians weren’t built for either at the speed Physical AI requires. Four gaps result: limited real-time access, compression that strips model-relevant signal, IT/OT fragmentation, and site-by-site architectures.

Summary InfluxDB 3.11 delivers major performance and data management updates for growing time series workloads, with significantly faster queries on recent data, expanded support for wide and ultra-sparse schemas, and a more predictable resource profile under load. InfluxDB 3 Enterprise also adds backup and restore, bulk Parquet import, row-level deletes, and a built-in Explorer UI.

Summary The InfluxDB satellite telemetry demo is a live mission-control application that monitors a simulated fleet of 12 satellites in real-time. It shows how the InfluxDB 3 Processing Engine can detect anomalies as data is written, enrich time series data with third-party data, and power a grounded AI agent using the InfluxDB 3 MCP server to investigate and explain fleet health.

Summary The five most recent plugins from the InfluxDB team are live: Sagemaker, value counter, Chronos forecasting, simple data replicator, and a stock portfolio tracker. Table of Contents The InfluxDB team has released five new Processing Engine plugins. They range from making it easy to call a hosted ML model to pulling in stock market data in real-time. Every one of them can be activated with a few terminal commands.

Summary InfluxDB plays a critical role in LeoLabs’ infrastructure, enabling a lean team to operate with confidence that potential issues will be detected and surfaced in real-time. By offloading the complexity of managing time series data at scale, engineers are free to focus on higher-impact work (such as optimizing their radar network) rather than maintaining and troubleshooting database systems.

Summary: Q2 was about giving teams more leverage with less overhead. Between April and June 2026, releases across Telegraf, InfluxDB 3, and InfluxDB 3 Explorer focused on reducing manual work and putting more control directly in their hands as they scale. Telegraf Enterprise reached general availability, giving teams a centralized way to manage, monitor, and support tens of thousands of Telegraf agents.

InfluxDB 3 Explorer 1.9 makes it easier to work with your existing queries. Whether you’re migrating Flux queries to SQL or you’ve been writing in InfluxQL for years, this release helps bring your existing queries forward instead of starting from scratch. For teams moving to v3 from earlier versions of InfluxDB, query migration is often one of the last major hurdles.

Telegraf Enterprise is now generally available. It combines Telegraf Controller, a centralized management console for Telegraf, with official support from InfluxData. Open source Telegraf remains unchanged. Telegraf Controller is free to start with built-in limits, while a Telegraf Enterprise license unlocks higher-scale limits, audit logging, LDAP/OIDC integration, and commercial support. Telegraf has become the standard for collecting telemetry across cloud, edge, and physical infrastructure.

Satellite operations depend on telemetry as the primary interface to systems that teams cannot directly inspect. Once a spacecraft reaches orbit, signals such as battery levels, temperature, signal strength, and fault codes become the foundation for understanding system health and maintaining control. Telemetry streams continuously, so the underlying data system becomes a critical control point that needs to handle a constant, heavy flow of data.