At some point if you’re working with data, you’ll probably want to be able to visualize it with different types of charts and organize those charts with dashboards. You’ll also need somewhere to store that data so it can be queried efficiently. One of the most popular combinations for storing and visualizing time series data is Grafana and InfluxDB.
With the release of InfluxDB’s new storage engine for InfluxDB Cloud, InfluxDB Cloud now supports SQL. This is because the updated InfluxDB uses the Apache Arrow DataFusion project as a key building block for its query execution engine. DataFusion’s sophisticated query optimizations support near unlimited cardinality data in InfluxDB Cloud.
Continuing in our series of InfluxDays recaps, we turn our attention to Brian Gilmore’s presentation on Industrial IoT. This is an area that uses time series data extensively and has a lot of room to expand the way it uses this data. Here’s a quick breakdown of where things stand today.
Most customers running Kubernetes clusters Amazon EKS are regularly looking for ways to better understand and control their costs. While EKS simplifies Kubernetes operations tasks, customers also want to understand the cost drivers for containerized applications running on EKS and best practices for controlling costs. Anodot has collaborated with Amazon Web Services (AWS) to address these needs and share best practices on optimizing Amazon EKS costs.
Time series data often comes in large volumes that need to be handled carefully to produce insights in near real time. We’re constantly moving through time. The time it took you to read this sentence is now forever in the past, unchangeable. This leads to something unique about data with a time dimension: It can only go in one direction. Time series data is different from other data for many reasons.
