Operations | Monitoring | ITSM | DevOps | Cloud

Early this year, we set out on a journey to onboard a new cloud engineering team at JFrog. Many can relate to the challenges involved with onboarding a new team, these were amplified even more during the pandemic. However this blog post is not about COVID-19, it is about sharing our experience of fine-tuning the onboarding path for this unbeatable group. TL/DR: What it takes to build and onboard a team of junior engineers into the existing JFrog Cloud engineering team.

CloudOps is on the up. This is in part due to the rapid acceleration of the shift to cloud that was caused by the pandemic. The shift allowed companies to innovate faster, enjoy greater flexibility and scalability, and become more cost efficient. Many organizations who rapidly adopted cloud or increased their usage now realize that they need to better manage their cloud investments in order to fully embrace these benefits.

Serverless lets you deploy applications far away in a data center of a cloud provider. This relieves you of the lion’s share of operational burdens. The more you buy into your cloud provider’s ecosystem, the less you have to do yourself: no more OS updates or database bugfix installations. But you still need to do some operation-related work on your own. For instance, monitoring your application to know what’s going on in that far away data center.

Splunk Cloud Architect Paul Davies recently authored and released the GCP Application Template, a blueprint of visualizations, reports, and searches focused on Google Cloud use cases. Many of the reports included in his application require Google Cloud asset inventory data to be periodically generated and sent into Splunk. But HOW exactly do you craft that inventory generation pipeline so you can "light-up" Paul's application dashboards and reports?

Running and troubleshooting production services requires deep visibility into your applications and infrastructure. While basic logs and metrics are available out of the box with Google Cloud Compute Engine (GCE), capturing advanced data used to require the installation of both a metrics agent and a logging agent.