With the speed of change in artificial intelligence (AI) and big data, podcasts are an excellent way to stay up-to-date on recent developments, new innovations, and gain exposure to experts’ personal opinions, regardless if they can be proven scientifically. Great examples of the thought-provoking topics that are perfect for a podcast’s longer-form, conversational format include the road to AGI, AI ethics and safety, and the technology’s overall impact on society.

Last month we announced InfluxDB IOx (pronounced: eye-ox), a new Rust and Apache Arrow based core for InfluxDB. While InfluxDB IOx is a columnar database built on top of object storage, that’s only part of its role. In addition to query functionality, one of the big goals of InfluxDB IOx is to help manage the data lifecycle for time series data. This post will cover the design for how InfluxDB IOx plans to manage that data lifecycle.

Now that you’ve read through Top 5 Hurdles for Flux Beginners and Resources for Learning to Use Flux, you’re well on your way to being an advanced Flux user. This post includes some of the top hurdles for intermediate Flux users as well as solutions.

Are you new to InfluxDB v2.0 and Flux? Are you intimidated by learning a new time series scripting and query language? Perhaps you’re an InfluxDB v1.x user and you’re familiar with InfluxQL, and you’re unconvinced that learning Flux is worth your while? This two-part blog series aims to demonstrate the power of Flux and walk you through solutions to the top hurdles for both new and old InfluxDB users.

Teams around the world are going through changes. With offices closed from Hong Kong to San Francisco, Zoom meetings are the new norm, and online platforms are the standard for collaborating and keeping businesses running as usual. We’ve written about distributed work and how doing distributed well requires the right tools. When a traditional office environment isn’t available, information naturally becomes fractured across multiple single-purpose platforms.

In this blog post you’ll learn how to create visualizations for Kibana dashboards from Jupyter Notebooks. The good news for you as a Python developer: You won’t have to spend time writing cumbersome plain JSON documents or even JavaScript code. Under the hood, we’re going to make use of Kibana’s Vega plugin which became generally available with Elastic Stack 7.10.

Linux system monitoring is necessary to ensure uptime, indicate system resource usage, and show which apps are consuming what percentage of resources. Monitoring Linux thereby enables users to properly manage applications and ensure the system is in good health.

As we approach the end of an unpredictable year, it’s a good time to reflect on the ways data has made a positive impact. Data is helping stop human trafficking with Global Emancipation Network, connected relief resources during crises with NetHope, and saved lives during wildfires with Zonehaven. And with our powerful partner ecosystem, and the arrival of the Data Age, there is so much more we can accomplish together.