Operations | Monitoring | ITSM | DevOps | Cloud

We all know the importance of cataloging, organizing, and breaking down the data in your logs. That process, parsing, makes information easier to find and simplifies subsequent analysis. Now, with Logz.io’s upgraded self-parsing tool, custom parsing rules, and log organization is easier than ever. What’s important is parsing that data out correctly. The better parsed, the easier to query.

Version 2.4 improves its e2e tests, revamps how logging in the HAProxy Data Plane API works, adds support for namespace filtering in Consul Service Discovery, improves runtime capabilities for maps and ACLs, adds server-template support and adds log_targets to global and defaults sections.

On Oct. 5, we hosted the first Grafana Virtual Meetup for an EMEA-based audience. Each Grafana meetup features “bite-sized” presentations from our user community and members of the Grafana Labs team. We want to provide opportunities (even virtually!) for members of our community to connect with one another and share what they’re working on or have learned.

Whether you are moving your applications to the cloud or modernizing them using Kubernetes, observing cloud-based workloads is more challenging than observing traditional deployments. When monitoring on-prem monoliths, operations teams had full visibility over the entire stack and full control over how/what telemetry data is collected (from infrastructure to platform to application data).

Here’s a common situation that plagues many development teams. You run an application through your CI/CD pipeline and all of the tests pass, which is great. But when you deploy it to a live target environment the application just does not function as expected. You can’t always predict what will happen when your application is pushed live. The solution?

Support for including default values in custom pipelines has been a highly requested feature. We are happy to announce that this feature is now live. Providing a default value helps avoid errors when you manually trigger a custom pipeline. If you often rely on the same value for certain variables, it can be frustrating to get a failed build when you forget to specify the value or have a typo when providing the value.

Bitbucket Pipelines provides a Docker caching feature that can help improve build times. However, the limitation is that only compressed caches under 1GB are saved and can be used. In this blog, we outline a process showing how you can use compressed caches that are larger than 1GB. With Docker versions >= 19.03, you can use the BuildKit feature. With BuildKit, you don’t need to keep the cache locally before building the Docker image since it caches each build layer in your image registry.

Like the historic space race, the competition to plant the flag of DevOps is blasting off. According to market intelligence firm IDC, global business will invest $6.8 trillion in digital transformation by 2023. Yet research also suggests that 70 percent of them will fail to meet their goals. JFrog was the first company to offer a universal, hybrid, end-to-end DevOps platform.

It’s 2 AM and you’re paged when you’re still awake – how well can you find what you need to fix the latest mistake? When the incident begins it might only be impacting a single service, but as time progresses, your brain boots, the coffee is poured, the docs are read, and all the while as the incident is escalating to other services and teams that you might not see the alerts for if they’re not in your scope of ownership.

Flexibility is one of the cloud’s biggest benefits, but also one of its biggest challenges. When you have different teams using resources in the cloud and deploying instances at the click of a button, your cloud environment could easily become chaotic. Without a definitive plan governing your cloud operations, your costs will inevitably spiral out of control. A cloud cost strategy is your action plan for managing costs and staying profitable while working and building products in the cloud.