Jupyter Notebooks are wonderful because they provide a way to share code, explanations, and visualizations in the same place. Notebooks add narrative to computation. The cells compartmentalize steps and reduce the fear or hesitation associated with editing code. In this way, notebooks act as an invitation for experimentation. Today, I want to extend that invitation and apply it to InfluxDB. In this post, we’ll learn how to query our system stats data from InfluxDB v2.0 using Flux.

At the end of 2019, we released a new Suricata input plugin with Telegraf 1.13.0. In this blog, I’ll talk about the the powerful combination of these two open source products — the importance of Suricata and why you should use Telegraf to monitor its performance. I wanted to start off first thanking Sascha Steinbiss for submitting this plugin. Here at InfluxData, we can’t tell you how much we value our open source community.

Today we’re excited to announce the general availability of InfluxDB Cloud for Google Cloud. With this new service, GCP users can now use our leading time series data platform on Google infrastructure. This lets you address a wide range of use cases: server monitoring, IoT sensor data tracking, real-time customer analytics, application performance metrics, network monitoring, security threat detection, and financial market analysis.

SAN FRANCISCO — February 4, 2020 — InfluxData, creator of the time series database InfluxDB, today announced the availability of InfluxDB Cloud on Google Cloud. The strategic collaboration, originally announced in April 2019, is part of a major Google Cloud initiative to make the most powerful open source technologies more accessible to its customer base.

Fast-growing products are not overnight successes contrary to what you often hear. At InfluxData, we’re on a mission to build a user base from scratch with our new flagship product InfluxDB Cloud. Every new user has to go through a signup flow to create their account. So it must go as smoothly as possible. User research and design experimentation are the way we’ll reach this goal, and the main ingredient in this recipe is you: the community member.

I recently spoke with Matthew VanTassel, CTO at Rise to learn how he’s using InfluxDB at home to make his house smarter, to improve his online gaming and to understand his garden better. Understanding the versatility of InfluxDB at home helped him realize the full potential of InfluxDB in his job.

I think that a lot of people immediately associate the word “time series” with “forecasting”. After all, who doesn’t want to be able to predict the future? While we can’t do that quite yet, we can produce forecasts with confidence intervals. In this tutorial, we’ll learn how to make a univariate time series prediction with Prophet and InfluxDB.

InfluxData prides itself on prioritizing developer happiness. A large part of maintaining developer happiness is providing client libraries that allow users to interact with the database through the language and library of their choosing. Data analysis is the task most broadly associated with Python use cases, accounting for 58% of Python tasks, so it makes sense that Pandas is the second most popular library for Python users.

Recently we released a new SNMP Trap input plugin in Telegraf 1.13. I’d like to tell you more about that plugin and how you can use it.