Operations | Monitoring | ITSM | DevOps | Cloud

We will focus on the audit logging of Google cloud itself, however, the input supports the below sources when it refers to Google Cloud Logs.

The arrival of Docker container technology brought with it an amazing array of capabilities. By encapsulating an entire software package, including its dependencies and libraries, into a single, portable container, Docker has made deployment across platforms such as AWS, Google Cloud, Microsoft Azure, and Apache a simple and straightforward process. When people talk about Docker, they probably talk about Docker Engine, the runtime that allows you to build and run containers.

Businesses are increasingly adopting distributed microservices to build and deploy applications. Microservices directly streamline the production time from development to deployment; thus, businesses can scale faster. However, with the increasing complexity of distributed services comes visual opacity of your systems across the company. In other words, the more complex your system gets, the harder it becomes to visualize how it works and how individual resources are allocated.

If you’re in the cloud engineering and DevOps space, you’ve probably seen the name OpenSearch a lot over the last couple of years. But, what is your current understanding of OpenSearch, and the components around it? Let’s take a closer look.

Any software application or a system can have bugs and issues in testing or production environments. Therefore, logging is essential to help troubleshoot issues easily and introduce fixes on time. However, logging is useful only if it provides the required information from the log messages without adversely impacting the system’s performance. Traditionally, implementing logging that satisfies these criteria in Java applications was a tedious process.

Modern, high-scale applications can generate hundreds of millions of logs per day. Each log provides point-in-time insights into the state of the services and systems that emitted it. But logs are not created in isolation. Each log event represents a small, sequential step in a larger story, such as a user request, database restart process, or CI/CD pipeline.

Observability has become a critical part of the digital economy and software engineering, enabling teams to monitor and troubleshoot their applications in real-time. Properly managing logs, metrics, traces, and events generated from your applications and infrastructure is critical for observability. A telemetry pipeline can help you gather data from different sources, process it, and turn it into meaningful insights.