Is your organization currently relying on an ELK cluster for log analytics in the cloud? While the ELK stack delivers on its major promises, it isn’t the only search and analytics engine — and may not even be your best option for log management. As cloud data volumes grow, ELK monitoring can become too costly and complex to manage.

In this post, we'll configure a Phoenix LiveView application to use a structured logger. We'll then use AppSignal to correlate log events with other telemetry signals, like exception reports and traces. Along the way, you'll learn about the benefits of structured logging, and you'll see how to configure a distinct framework and application logger in your Phoenix app. Let's get started!

Gone are the days of painful plain-text log management. While plain-text data is still useful in certain situations, when it comes to doing extended analysis to gather insightful infrastructure data – and improve the quality of your code – it pays to invest in reliable log management tools and systems that can empower your business workflow. Logs are not an easy thing to deal with, but regardless is an important aspect of any production system.

The success of businesses like Zoom, DropBox, and Slack demonstrates the power of product-led growth (PLG) as a strategy for scaling software companies in 2023. Central to this approach is event analytics, the practice of analyzing event data from a software product to unlock data-driven insights. Companies following a PLG strategy (“PLG companies”) use this data to inform product development decisions to enhance user experiences and drive revenue.

Logging plays a critical role in every application. In this tutorial, we’ll explore how to use Apache Log4j 2.x and highlight best practices that can help you in getting started and improve logging capabilities. Additionally, we’ll discuss various key features and improvements of Log4j 2.x over its predecessor, Log4j 1.x.

In the rapidly shifting world of financial technology (FinTech), acquiring and retaining new customers to achieve long-term business growth requires a proactive approach to user experience and application performance optimization. As FinTech companies compete against rivals to grow a user base and revolutionize how consumers manage their finances, they increasingly depend on data-driven insights to optimize their mobile applications and deliver exceptional user experiences.

Not even Tim Berners-Lee, the inventor of the world wide web, could have predicted the pace or breadth of its expansion over the last 30 years. Once, the web was only accessible via clunky desktop computers: today, it can also be reached from laptops, tablets, mobile phones and even smartwatches. What’s more, consumers want native apps that are unique to a particular platform and they expect updates to be provided seamlessly.

Organizations are leveraging log analytics in the cloud for a variety of use cases, including application performance monitoring, troubleshooting cloud services, user behavior analysis, security operations and threat hunting, forensic network investigation, and supporting regulatory compliance initiatives. But with enterprise data growing at astronomical rates, organizations are finding it increasingly costly, complex, and time-consuming to capture, securely store, and efficiently analyze their log data.

Do you know that log files in Linux can quickly consume disk space if not managed properly? This can lead to performance issues and even system crashes. Log files? What exactly are they, and why should they matter to anyone using Linux-based systems? Log files are essential components of any Linux-based system. They are text files that contain information about system events, including errors, warnings, and other important messages.

A data lake is the perfect solution for storing and accessing your data, and enabling data analytics at scale - but do you know how to make the most of your AWS data lake? In this week’s blog post, we’re offering 10 data lake best practices that can help you optimize your AWS S3 data lake set-up and data management workflows, decrease time-to-insights, reduce costs, and get the most value from your AWS data lake deployment.