Operations | Monitoring | ITSM | DevOps | Cloud

I’ve been getting some version of this question a lot lately, mostly in our Seer preview webinars. Different audiences, same handful of questions: Worth answering all three in one place. Honestly, I needed to write this down for myself too. Things are moving fast around all of us and answers seem to get more nuanced by the week. This is an attempt to codify the difference: what each option is and when it makes sense to reach for one over the other.

Databricks is a platform for analytics, AI, and data intelligence that has pioneered the lakehouse paradigm in modern data systems.

In the first part of this series, we cataloged key metrics for Databricks data engineering, analytics, and Model Serving workloads. In this post, we’ll discuss how to collect those metrics and other telemetry data from Databricks and Apache Spark, which powers Databricks under the hood. We’ll cover collecting and querying telemetry data via system tables, as well as the other primary sources of visibility into.

Earlier in this series, we covered key metrics for monitoring performance in Databricks and discussed Databricks’ native resources for accessing those metrics and other key observability data, such as logs and data lineage. In this post, we’ll cover using the Databricks integration to bring that data into Datadog and monitor your Databricks analytics and AI/ML workloads alongside the rest of your end-to-end data pipelines and distributed infrastructure. We’ll show you how to.

Network diagnostics is the process of finding out why a network performance issue is happening: what's causing it, where and when it occurred, and who needs to fix it. It's the step between an alert telling you something is wrong and a fix being applied. For most IT teams, it's also the slowest part of resolving a network issue.

September 2026 brought a steady run of disruptions across e-commerce, AI, developer tools, education platforms, and cloud infrastructure. StatusGator’s Early Warning Signals often surfaced these incidents well before providers posted official updates.

Azure Monitor doesn't have a price. It has a bill and those are very different. There's no plan tier to pick, monthly set fee, or simple number to sanity-check against your budget. Instead, you're charged across a handful of separate meters: log ingestion, log queries, retention, alert rules, web tests, and custom metrics. Each of these metrics scale independently. Most teams don't see the full cost until the bill arrives.