InfluxDB is the leading time series platform. It’s About Time. ⏱️
San Francisco
Joined October 2013
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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
The #InfluxDB 3 downsampling plugin automates time series rollups, turning millions of high-frequency data points into structured summaries that keep queries fast and storage costs predictable as systems scale.
📈 bit.ly/4ox5Rer
AI doesn’t fail in real-world systems because models are weak. It fails when systems can’t see clearly enough to act in time.
For teams running live infrastructure, whether it’s devices, networks, or distributed systems, resolution matters. High-resolution time series data is
Sample data never pages anyone at 3am.
Our live demos run #InfluxDB on realistic, simulated workloads, so you can watch it handle data that behaves like the telemetry you actually work with. Two are up now:
🛰️ Satellite telemetry for spacecraft operations
🖥️ Data center monitoring for infrastructure teams
Pick the one closest to what you’re building: bit.ly/4yFoSR5
We’re hiring a Developer Advocate: demos, talks, workshops, and being the developers’ voice inside InfluxData.
Remote in the US or Canada. #InfluxDB
➡️ bit.ly/4sfAKHG
Stop overpaying for idle cloud resources or scrambling when traffic spikes!
#InfluxDB scales storage and compute automatically with your workload. You only pay for what you actually use, and your team stays focused on building instead of provisioning.
The v3 JavaScript client gives you programmatic access to write and query data in #InfluxDB 3.
Get started quickly and build exactly what you need! 🔧 youtube.com/watch?v=vT5OLqTr…
One config change shouldn’t mean updating hundreds of Telegraf agents individually.
Telegraf Controller 1.1 adds new ways to manage configuration fleet-wide: versioning, global constants, aliases + config groups.
Plus HA for Enterprise.
Learn more: bit.ly/4hOn3wm #InfluxDB
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 A cockpit with numerous control panels and displays is shown. To the left, white text on a blue background reads, "The High Stakes of Aerospace Reliability." The InfluxData logo is in the upper left corner.
AI requires formidable data infrastructure, especially at enterprise scale. Are we ready?
#InfluxDB developer advocate @influxcole speaks to @joemckendrick for @dbtrends about the need for a clean and well-defined data model to serve as documentation for AI.
Read the article to find out how the experts are approaching this paradigm shift. bit.ly/4bzujYh
When a 3rd-party breach exposed gaps in SaaS visibility, our security team built an in-house monitoring platform powered by time series telemetry.
Now we catch anomalies across their supply chain in real-time: bit.ly/4p7AMiK via @DarkReading #InfluxDB
ALT A humanoid robot is looking through binoculars. The robot has a white and silver exterior with visible mechanical components. The background is a gradient of light blue.
Every downsampling job, webhook, and anomaly check running outside your database is one more pipeline to babysit.
Tomorrow, learn to run them as Python plugins inside #InfluxDB 3 with the Processing Engine in a training hosted by @influxcole
🗓 Oct 22 | 8AM PT | 3PM GMT
ALT Sure! Here's an alternative text description: "Promotional image for a training event titled 'Intro to the InfluxDB 3 Processing Engine' by InfluxData. Scheduled for October 22, 2026, at 8 AM PT / 4 PM BST. Features a small circular photo labeled 'Cole Bowden, Senior Developer Advocate.' Includes a 'Register Now' button.
From @CiscoLive to a remote construction site to data centers, @Cisco uses #InfluxDB to turn telemetry into operational decisions.
📡 Monitor 2,300 APs and 600+ switches during a live event
🏗️ Track ~30 sensor parameters with limited connectivity
⚙️ Stream network telemetry with Telegraf gNMI
Three very different environments. One time series foundation.
Read the story: bit.ly/3ORKbOB
Cardinality limits are a thing of the past.
#InfluxDB 3 eliminates the series index that made high cardinality a memory bottleneck. Keep the device IDs, user IDs, and metadata that make your data useful. Millions of unique series are part of the job.
Hit play to see the architecture behind unlimited cardinality.
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 👇
youtube.com/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.