Logstash plays an extremely important role in any ELK-based data pipeline but is still considered as one of the main pain points in the stack. Like any piece of software, Logstash has a lot of nooks and crannies that need to be mastered to be able to log with confidence. One super-important nook and cranny is the Logstash configuration file (not the software’s configuration file (/etc/logstash/logstash.yml), but the .conf file responsible for your data pipeline).

Famed management thinker Peter Drucker is often quoted as saying, “You can’t manage what you can’t measure.” Tracking and analyzing data of a system provides metrics to measure, predict, and improve the underlining health of the system. Logging data is the simplest act of collecting data for measurement and plays an important role in modern enterprises, as it provides a way to measure the health of hardware devices and software applications alike.

Scenario You are running a large production environment with many Windows servers. There are multiple forests in the network and some forests have multiple domain controllers. Your Windows server security is paramount – you want to track and audit suspicious activities and view detailed Windows reports extracted from the Windows servers event logs.

Organizations that depend on distributed systems often write their applications in Go to take advantage of concurrency features like channels and goroutines (e.g., Heroku, Basecamp, Cockroach Labs, and Datadog). If you are responsible for building or supporting Go applications, a well-considered logging strategy can help you understand user behavior, localize errors, and monitor the performance of your applications.

Windows logs can be very informative, providing a perfect picture of the activities happening on an endpoint. Unfortunately, the logs can also be hard to decipher when you first start examining them. Graylog uses Pipelines and Data Adapters to enrich logs to make them more functional and easier for you to read.

Java programs running on JVM create objects on the heap. At some stage, these objects are no longer used and can pile up as “garbage” needlessly taking up memory. Replacing the manual process of explicitly allocating and freeing memory, the Java Garbage Collection process was designed to take care of this problem automatically.

Don’t you just hate it when APIs are failing and you have absolutely no clue why? Now imagine you don’t have access to the VM, cluster or container where your software is running. Want me to continue with this nightmare? Yes, that’s what debugging AWS Lambda functions tends to seem like. A horrid nightmare of not knowing what is happening nor why things are failing. This article will show you a way of logging function invocations.

They say knowledge is power and today, there is no greater source of knowledge than data. It is well documented that businesses produce a vast amount of data whether it be in manufacturing, IT, UX, Marketing, Sales or Finance. Collectively, all of this data can provide you with business intelligence, enabling you to optimize decisions, improve collaboration, and increase overall performance.

Alerts are a core part of monitoring systems. Using alerts keeps you aware of changes within your infrastructure and applications, helping you identify and respond to issues faster. Log management solutions like LogDNA provide an ideal environment for configuring alerts, since it allows you to create detailed alerts based on your log data. Rather than manually search for problems, you can use alerts to scan your log data in real-time and receive immediate notifications on potential problems.

One of the unintended side-effects of the public cloud is the enablement of a platform-agnostic cloud architecture. This means that it’s now easier than ever to mix offerings from multiple vendors (cloud vendors in this case) to form a single application or service-based solution. There is one gotcha, however: log management.