Operations | Monitoring | ITSM | DevOps | Cloud

Much of the log data we handle doesn’t offer substantial insight and can be conveniently removed from your logs, helping us reduce costs. What may seem like a small adjustment, like deleting an attribute, can have significant implications when scaled up. A typical case involves fields in your logs presenting empty values or housing data considered irrelevant. Below we’ll take a look at a few examples of what this looks like and how you can take action in BindPlane OP.

I’ve been working with Grafana Tempo for about half a year now, and one thing I like about it is that Tempo requires only object storage for storing traces, which is easy to set up in both cloud environments and on-premises. Another outstanding feature is TraceQL, which allows searching for relevant traces with a powerful query language.

The process of adding new data to operations and security analytics tools is familiar to admins. New data onboarding can be a tiresome process that takes up too much time and delays getting value from the new data. The process typically begins with the admin engaging the data source owner, getting the wrong data sample, and then having to try again.

The last few years have seen a big change in the size, make-up, and nature of database estates. Data is growing both in volume and complexity, it is now normal to have workloads as well as data in multiple public clouds, and organizations are increasingly using different kinds of databases for different use cases.

Okay, we are back for Part 2! Last time we discussed the new community Python library for InfluxDB 3.0. Let’s talk about a bolt-on application that uses the client library as the core of its development, the InfluxDB 3.0 Python CLI.