Observing systems has evolved over the years, and Datadog has been there helping it along. The way agents deliver data to predictable and information-rich dashboards is very nice. There is a lot to be said about being predictable and I don’t think anyone would argue about Datadog being predictable on all fronts, except maybe cost.
In this post, I’m going to use the persona frame from the baseline post and walk the same three people through Datadog.
[Read More]In a similar way to Honeycomb being “a great way to store and retrieve telemetry”, someone noticed that Snowflake was a great way to store and retrieve telemetry. Observe was born out of that concept, has one datastore with everything poured into it and shaped on the way through. No silos is the whole pitch.
In this post I’m using the persona frame from the baseline post and walking the same three people through Observe.
[Read More]Each observability tool tends to lead users toward a specific workflow. In the same way that working with computers on Windows or MacOS has very different starting points, Logs, Metrics, and Traces each have several entry points.
I want to dig into the benefits and drawbacks of each default workflow.
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