If you are working with large amounts of data that will primarily be used for analytics, a column database might be a good option. There are a lot of different options when it comes to choosing a database for your application. A common discussion seems to be the high-level SQL vs. NoSQL database argument of whether data should be stored in a relational database or in a NoSQL alternative like key-value, document or graph databases.
With native SQL support coming to InfluxDB, we can broaden the scope of developer tools used to analyze and visualize our time series data. One of these tools is Apache Superset. So let’s break down the basics of what Superset is, look at its features and benefits, and run a quick demo of Superset in action.
Red Hat is a global leader for open source enterprise IT solutions with a portfolio of products that includes hybrid cloud infrastructure, middleware, cloud-native applications, and automation solutions.
At this year’s InfluxDays event, the capabilities of InfluxDB took center stage. It’s not enough to simply deploy a technology platform and hope people will use it. This isn’t a Kevin Costner movie. That’s why it’s helpful to talk about specific use cases, their typical challenges, and how InfluxDB can address those challenges. Fortunately, that’s just what Influxer Charles Mahler did for network monitoring.
I recently chatted with one of our InfluxDB Cloud customers, Rune Labs, to discuss how they’re using this purpose-built time series platform. Every customer has a unique story — I love sharing their stories as well as their Telegraf, InfluxDB, and Flux tips and tricks. Keep reading to learn about Rune Labs’ approach to precision neurology, and learn from Engineering Manager Carolyn Ranti how they are using InfluxDB to collect sensor data.
