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.

“If you can’t measure something to get results, you can’t possibly get better at it. Worse yet, you won’t know what you should be focusing on,” says Dennis Brazil, Sr. Engineering Manager, SRE Monitoring at PayPal. Brazil and his team needed a scalable end-to-end host monitoring solution to keep pace with the company’s infrastructure modernization to a container-based architecture.

2019 was a great year for InfluxData. But before I get into the details, I need to emphasize that none of this was possible without the incredible open source community around InfluxDB, the customers that rely on our products, and the investors that believe in our team.

My holiday challenge to explain serverless InfluxDB to my family produced a useful Flux script anyone can put to work today. Before we dive into the code, let me outline the high-level approach to gathering and visualizing how long it takes a website to respond to an HTTP request.

Today we announce InfluxDB 2.0 Open Source’s official move to Beta. This represents a huge step forward from where we started out nearly a year ago and is one step closer to general availability. You can download the latest version on our downloads page. Since we announced the first Alpha for InfluxDB 2.0 back in January ‘19, we have been working hard to build out and harden InfluxDB 2.0’s capabilities.

If money makes the world go round, then technology is becoming its engine. Financial services, in particular, have widely adopted big data technologies and analytics to inform better investment decisions. The past decade has seen the rise of digital investment platforms. Financial institutions — facing increasing competition, regulatory constraints and customer needs — are seeking ways to leverage technology to gain efficiency and competitive advantage.