Systems | Development | Analytics | API | Testing

Today enterprises can leverage the combination of Cloudera and Snowflake—two best-of-breed tools for ingestion, processing and consumption of data—for a single source of truth across all data, analytics, and AI workloads. But now AWS customers will gain more flexibility, data utility, and complexity, supporting the modern data architecture.

One of the major challenges in detecting phishing is the limitations at the Internet Service Provider (ISP) level. Traditional tools often lack the visibility and ability to recognize phishing sites in real time, as they mostly rely on network-level information. Phishing schemes frequently use complex, changing tactics, like rotating domains or imitating legitimate sites, which can go unnoticed without a detailed analysis of the site’s content.

Platform engineering is a software development approach that combines DevOps principles to elevate the developer experience. It focuses on designing and constructing toolchains and workflows that promote self-service capabilities for software engineering teams. By addressing security, compliance, and operational efficiency, platform engineering enables organizations to build robust internal systems tailored to the needs of both developers and operations teams.

Efficient handling of asynchronous data streams is an important tool of modern application development. Kotlin Flows, part of the Kotlin Coroutines library, provide a flexible and elegant solution for working with such data streams. Kotlin Flows are part of Kotlin Coroutines – unlike traditional callbacks or RxJava handling, which can be clunkier and may not directly integrate with your existing code structure easily.

Ever commit code and wonder how it’s faring in the pipeline? Let's make every status update as clear as day. Every time you push new code, a series of automated CI builds and checks spring into action, each telling a part of your project's story. This is where git commit statuses come in—they transform the complex results of these processes into clear, actionable insights. Think of them as real-time signals that indicate whether your code is ready to go live or needs a little more attention.

In this article, you'll learn about N+1 queries, how to detect them with AppSignal, and how to fix them to speed up your Django apps significantly. We'll start with the theoretical aspects and then move on to practical examples. The practical examples will mirror scenarios you might encounter in a production environment. Let's get started!

Releasing a beta version of a Node.js package is a common practice when you want to share a new feature or a breaking change with testers or early adopters without affecting your stable users. Beta releases are tagged with a special version identifier (like 1.0.0-beta.0), making it clear that the version is pre-release and not production-ready. In this guide, we will cover the steps to release a beta version of your package using npm, ensuring a smooth and controlled release process.

Organizations are in a sheer need to stay up-to the minute, and be ahead for protecting their systems and data. Penetration testing can prove to be a structured process that can mimic the real-world vulnerabilities before it’s prone to any malicious exploits. In this entire lifecycle of pentesting, we got some tools in order to automate, streamline, and enhance each phase of it. They could be of various types, based on their specific role in this entire lifecycle.

Whether you’re struggling to pinpoint why your bounce rate is off the charts, understand which pages drive conversions, or optimize the user journey, the right website analytics tool can make or break your strategy. But with countless platforms claiming to be the best, choosing one feels overwhelming. That’s why we’ve handpicked the top 15 website analytics tools for 2025 to help you track the metrics that matter and grow your business smarter—not harder.

Democratizing and sharing data inside and outside your organization, as a real-time data stream, has never been more in demand. Treating data as-a-product and adopting Data Mesh practices is leading the way. Here, we explain the concept through a real-life example of an airline building applications that process data across different domains.