Systems | Development | Analytics | API | Testing

I'm commonly asked by customers for advice on how they can build a good platform cross-charging model for their organization. And my gut reaction is nearly always "don't." We'll come back to why I think that later, but first let's look at what cross-charging means, why you might want it, and how it can be designed.

Modern reporting tools like Tableau and Power BI have transformed how end users visualize and analyze data. But for developers and analysts relying on REST APIs to connect these platforms to their data sources, frustrations often mount. From inconsistent data formats to performance bottlenecks, REST APIs—while powerful—weren’t built to handle the complex, high-volume demands of modern BI environments.

2025, developers use advanced tools and strategies to debug apps running in distributed systems, microservices, and modern frameworks like Flutter, React, and SwiftUI. Here’s a quick summary of the best practices to make remote debugging faster and more secure.

The manufacturing industry can benefit from AI, data and machine learning to advance manufacturing quality and productivity, minimize waste and reduce costs. With ML, manufacturers can modernize their businesses through use cases like forecasting demand, optimizing scheduling, preventing malfunctioning and managing quality. These all significantly contribute to bottom line improvement.

Providing your analytics users the ability to get answers from their data is useful, but only if the solution can guide them to ask the right questions. The rising integration of artificial intelligence (AI) and machine learning (ML) in many business intelligence (BI) solutions has enabled new innovative approaches in combining natural language techniques with self-service analytics, resulting in helpful NLQ tools.

There are few things that can derail developer productivity and undermine your pipeline like a flaky test. Testing is the backbone of a good development process, ensuring that your code is as accurate and usable as possible. When these tests point towards faulty development, the impacts can be significant. This information is predicated on an assumption, however – the assumption that what the test says is accurate.

Most engineering teams have infrastructure monitoring nailed down—they are tracking uptime, latency, and error rates, and have set up alerting in places. But API issues don’t always start there. Infrastructure metrics don’t tell you how your API users experience your API. A critical integration may have been repeatedly facing failures due to invalid authentication tokens. A new version you have deployed might have introduced a subtle schema change that breaks older clients.

Data masking protects sensitive information by replacing real data with realistic, fictitious values. It’s an essential technique for securing data, reducing breach risks, and meeting compliance standards. Let’s explore the fundamentals of data masking, its importance, common methods, and some real-world examples of how it’s used.

JSON (JavaScript Object Notation) is a fundamental component of modern web development. Its simplicity and readability have made it a universal data interchange format, used across a wide range of industries and applications. The straightforward structure of JSON, which is both human-readable and machine-parseable, has contributed to its widespread adoption.