Part 1 of the series of posts which talk about engineering design decisions to make high cardinality work in time-series databases.

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.

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.

Armed with the right PostgreSQL monitoring tools, database administrators and developers can identify potential bottlenecks, troubleshoot problems and make informed decisions to optimize their database environments. Monitoring PostgreSQL databases provides invaluable insight into their performance, health and overall efficiency.

What is Graphite? Simply put, Graphite is an open-source enterprise-ready time-series database. So what is a time-series database? Well, a time series is a series of data points indexed (or listed or graphed) in time order. Time Series databases have excellent benefits over traditional databases in terms of high performance, higher writes, improved scalability, better reliability, and many more.

OpenTelemetry can be used to trace Gin applications for performance issues and bugs. OpenTelemetry is an open-source project under the Cloud Native Computing Foundation (CNCF) that aims to standardize the generation and collection of telemetry data like logs, metrics, and traces. Gin is an HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin!