Artificial intelligence (AI) is now well and truly mainstream. Once the preserve of futurists and doom-mongers muttering about job losses, it has become a global watercooler topic thanks to the rise of ChatGPT, which promises to transform the way the entire world goes to work. But AI has been a vital tool in the developer’s armoury for a while. It has given us new ways to optimize workflow and focus on those high-level tasks that will be forever human.

The flood of software innovation over the past 20 years would not have been possible without Agile working. The concept of releasing fast, taking feedback and building back better has birthed the iPhone, social networks and the Cloud. The world would be a slower, duller place without it. But we can’t do Agile unless we can get real insights from our user’s screens, and here, the developer tools published by Safari, Chrome and other browsers are crucial.

Once, your users may have forgiven a bug in your app. Today, they likely won’t. Today’s consumers, many of them Gen-Zers who’ve been using gadgets since they learned their hands, expect a mobile experience that’s swift, seamless and secure. And with page speeds increasing all the time, lags and snags are no longer acceptable. Which means our apps need to glitch-free right out of the gate.

What’s the right way to manage growing volumes of enterprise data, while providing the consistency, data quality and governance required for analytics at scale? Is centralizing data management in a data lake the right approach? Or is a distributed data mesh architecture right for your organization? When it comes down to it, most organizations seeking these solutions are looking for a way to analyze data without having to move or transform it via complex extract, transform and load (ETL) pipelines.

As enteprise networks grow larger and more complex, IT teams are increasingly dependent on the enhanced network visibility and monitoring capabilities provided by log analytics solutions. Log analytics gives enterprise Engineering, DevOps, and SecOps teams the ability to efficiently troubleshoot cloud services and infrastructure, monitor the security posture of enterprise IT assets, and measure application performance throughout the application lifecycle or DevOps release pipeline.

As more organizations are moving to a cloud-native architecture, there is an ever-increasing need to monitor applications and services. Logging is a crucial part of this process, as it provides the insights and visibility to identify potential issues and track application performance. When it comes to managing and monitoring applications, Docker logging is an essential part of the process.

Most people only realize the necessity of logs when they need them the most. But when your application breaks, user complaints start flooding in, and you have no clue how to fix it, it's too late to add some log messages that might have helped. Good logs pay for themselves tenfold. They make it a breeze to diagnose those tricky bugs, and if you do logs right, they can alert you of issues even before your users notice. But what does it mean to 'do logging right'?

If you ever had to analyze an issue in production, I’m sure you know how important it is to have good logging. Good logging requires three things: While you still need to decide yourself which log messages you should write for each use case, you don’t need to worry about requirement 2 and 3. Various logging frameworks already solved these technical requirements. You only need to choose one of them and use it to write your log messages.

We are going to look at how JMeter outputs to both the log panel in GUI mode and the log file in non-GUI mode. We will look at the properties relating to the GUI log panel and the Appenders and Loggers that determine what data is output and at what level the logs are output at. JMeter uses log4j to provide its logging mechanism and from the log4j website: We will look at how Jmeter configures Appenders and Loggers separately but they work together to produce the logged output.

Wondering how to create a dashboard in Kibana to visualize and analyze your log data? In this blog post, we’ll provide a step-by-step explanation of how to create a dashboard in Kibana. You’ll learn how to use Kibana to query indexed application and event log data, filter query results to highlight the most critical and actionable information, build Kibana visualizations using your log data, and incorporate those visualizations into a Kibana dashboard.