Systems | Development | Analytics | API | Testing

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.

This article will teach you how to monitor Ruby errors without using a service like Honeybadger! Learn about Ruby's built-in errors, how to use log levels and tagging to customize the log output, display errors on the front-end, and more.

Logs are essential to any application's development. Most Ruby logs are verbose and chunky, so digging for exactly what you need can be difficult. Even though they contain useful information, you might not get as much value as you should from logs if you don't know how to use them effectively. In this article, we'll explore: Let's get started.

Access to data is critical for SaaS companies to understand the state of their applications, and how that state affects customer experience. However, most companies use multiple applications, all of which generate their own independent data. This leads to data silos, or a group of raw data that is accessible to one stakeholder or department and not another.