Time series deployments rarely grow as planned. We built InfluxDB 3.11 for that complexity. Now, 3.12 gives enterprise deployments more room to scale:
✓ Bulk Parquet import 3x faster
✓ Distributed compaction (beta)
✓ Per-database schema enforcement
✓ RBAC, now GA
bit.ly/46VLGk1#influxdb
Time series isn’t just about the past. It’s your window into what’s happening 𝙧𝙞𝙜𝙝𝙩 𝙣𝙤𝙬.
Discover how a modern time series database turns metrics into real-time decisions that matter: bit.ly/44kSXJH#InfluxDB
More sensors. More telemetry. Higher cost?
With pay-per-tag pricing, that can be the reality for manufacturers relying on legacy data historians.
In @ManufactureDive, we break down three ways legacy historians can limit modern manufacturing, and why architectures designed for high-volume time series data offer a different path to scale. #InfluxDB
Read the full story: bit.ly/3RwAyWV
Let’s be honest: getting the size of your deployment right is what makes or breaks real-world performance and cost.
This blog on Amazon Timestream for #InfluxDB 3 shows you how to match your workload to the right deployment strategy, so you’re not overpaying or underpowered.
If you're planning a deployment or optimizing an existing one, this guide is worth a look. 👇 go.aws/4uFkfF5
Time series deployments rarely grow as planned. We built InfluxDB 3.11 for that complexity. Now, 3.12 gives enterprise deployments more room to scale:
✓ Bulk Parquet import 3x faster
✓ Distributed compaction (beta)
✓ Per-database schema enforcement
✓ RBAC, now GA
bit.ly/46VLGk1#influxdb
ALT A digital banner for InfluxDB 3.12 by InfluxData, highlighting features like distributed compaction, three times faster imports, and increased control. The design features a dark blue background with large, teal digit "3.12" in a pixelated style.
SQL or Python for downsampling?
With #InfluxDB 3, use SQL for flexible, query-time summaries or the Python Processing Engine to materialize rollups on a schedule.
This guide walks through both approaches step by step: bit.ly/4ce7JFI
ALT Image showing two graphs with fluctuating data lines labeled "A Guide to Downsampling: Time Series Data with InfluxDB 3" at the top, alongside the InfluxData logo.
The #InfluxDB 3 Enterprise + @Litmus_io edge integration replaces legacy historians with real-time visibility + scalable historical analysis in a single unified architecture.
Watch the interview 👇
piped.video/watch?v=yKY8hqws…
🤖 Physical AI demands more from industrial data.
Training needs detailed history. Inference needs real-time telemetry. Both need to work across distributed environments.
Traditional historians weren’t built for this.
Discover 4 architectural gaps and how #InfluxDB 3 helps bridge them to power AI from training to action at the edge.
Read the article 👇 bit.ly/4yzYLed
ALT A dark blue graphic features the InfluxData logo and text that reads: "Where Historians Fall Short for Physical AI.
How do you fix a satellite 40,000 km away? You don't. You predict the failure first.
@Thales_Alenia_S is leveraging continuous time series telemetry and ML models to power true predictive maintenance. They keep critical satellites operational where physical repairs are simply impossible.
bit.ly/4if3ADk#InfluxDB
ALT The image shows a cockpit filled with numerous controls and displays, with the text "The High Stakes of Aerospace Reliability" on the left. The InfluxData logo is in the upper left corner.
AI agents are only as useful as the data they can access.
The official #InfluxDB 3 MCP Server connects Claude, ChatGPT, and other AI agents to InfluxDB 3 using natural language.
New in v1.3.0:
✅ Protocol compliance testing
✅ Expanded integration tests
✅ Improved npm packaging
✅ Support across all InfluxDB 3 editions
Read more: influxdata.com/blog/influxdb…
ALT A promotional graphic for InfluxData's InfluxDB 3 MCP Server v1.3.0, featuring text: "AI Access to Time Series." A flowchart shows a process: "Investigate recent BESS alerts" leading to a database query: "SELECT * FROM alerts WHERE time > now() - interval '15 minutes';" The background is a dark blue with connecting lines and nodes.
Physical AI is powering LEO satellites and Mars missions, but it needs high-fidelity time series data to work.
Thousands of sensor readings per second feed models that make deterministic decisions, not probabilistic guesses.
The leaders in space are building on purpose-built time series infrastructure to power real-time monitoring, autonomy, and predictive anomaly detection.
Turning telemetry into intelligence is what will set the next generation of space systems apart: bit.ly/3L1qnFR by #InfluxDB CEO @evankaplan via @SpaceNews_Inc
We're honored to be named one of the Coolest Database System Companies in CRN's 2026 Big Data 100. 🎉
As AI increases demand for real-time, operational data, we're proud to help organizations unlock the value of time series data at scale with #InfluxDBbit.ly/4oMqLbd
Join the @ApacheDataFusio community in San Francisco for an evening of lightning talks, mingling, and snacks. Co-hosted by InfluxData’s @andrewlamb1111 this meetup will spotlight real-world applications, performance innovations, and evolving integrations.
🎙️ Speaker lineup coming soon!
📍 Thursday, November 12 | 5:30 PM - 8:30 PM
Come for the talks, stay for the community, and leave with new ideas.
Get the date on your calendar: bit.ly/3VMvWgV
Real-time analytics or historical analysis? With the right architecture, you don't have to choose.
A new @AWSCloud blog demonstrates a dual-path IoT architecture that combines streaming and batch processing, with Amazon Timestream for #InfluxDB 3 powering low-latency time series analytics.
Read more: go.aws/4fOM2hx
InfluxDB + @Grafana = a tried-and-true pairing for time series monitoring. With #InfluxDB 3, connecting the two is simpler than ever. No third-party plugin or new query language required.
Our latest guide shows you how to go from data ingestion to a real Grafana dashboard using a realistic smart-building dataset.
Read the guide: bit.ly/4yZRzJ9
#InfluxDB lets you enforce schemas at the bucket level when you need structured time series data.
Learn how explicit schemas work and when to use them for your data model. 🎯
piped.video/MkbhvurkekE?si=0gHv…
30,000+ nodes monitored. 500,000+ hours of ONTAP testing supported monthly. ~1 minute to bring a new host under monitoring.
Explore how @NetApp uses Telegraf, #InfluxDB + Grafana to turn telemetry at scale into proactive incident response: bit.ly/4ycDYO0
ALT The image features the logos of InfluxData and NetApp against a digital wave background.
Five new #InfluxDB Processing Engine plugins have landed.
From SageMaker inference to Chronos forecasting, these additions expand a growing library for data transformation, downsampling, anomaly detection, alerting, and more, all running inside your existing InfluxDB instance.
Watch the video 👇
InfluxData CEO @evankaplan sat down with @JamesMaguire to explain how time series data has become foundational for the modern enterprise.
He detailed how specialized databases outperform general-purpose systems for fast, high-resolution data, why devs must increasingly start projects with time series in mind, and how #InfluxDB 3 represents a total architectural overhaul built for scale, performance, and real-time intelligence: bit.ly/4q1sYPx
New to #InfluxDB 3 Core or Enterprise?
The launch hub has tutorials, videos, and technical walkthroughs to get you from zero → real-time data fast.👇 bit.ly/42zzR0e
ALT The image is a promotional graphic for InfluxDB 3 Core & Enterprise by InfluxData. It announces their general availability and highlights features: InfluxDB 3 Core as an open-source, high-speed recent-data engine, and InfluxDB 3 Enterprise as a high-performance, fully-featured database for production. The background is dark blue with binary code.
One developer directed agents through 6,778 commits and 1M+ lines of Rust in 11 days.
@pauldix on why the harness and verification behind the Bun rewrite matter more than the language switch, and what happened when he tried it on #InfluxDB: bit.ly/3UtkQgs