In the first part of this series, we created a Django online shop with htmx. In this second part, we'll handle orders using Stripe.

Implementing a modern, integrated data architecture can help you break down data silos, which cause C-suite decision-makers to lose 12 hours a week. Furthermore, more than 60% of organizations agree that data silos represent a significant business challenge. The solution is a data architecture that eliminates silos, and that’s where the data mesh vs. data fabric debate comes in. While both data architectures work to eliminate data silos, they differ in their approaches (more on that later).

Although GoMock is one of the most popular ways to create mocks in Golang, it’s important to understand the broader landscape of mocking methods in the language to fully leverage its potential value in testing. Mocking is a powerful technique that allows developers to isolate their code, improve test reliability, and ultimately enhance overall code quality.

When it comes to managing AI projects, the Command Line Interface (CLI) can be a powerful tool. With ClearML, the CLI becomes an essential resource for creating job templates, launching remote for JupyterLab, VS Code, or SSH development environments, and executing code on a remote machine that can better meet resource needs. Specifically designed for AI workloads, ClearML’s CLI offers seamless control and efficiency, empowering users to maximize their AI efforts.

System administrators, DBAs, and application developers all know about the need for data integration. That’s when you have to pull data from one database, application, or web service to another. Fortunately, this task is a lot less challenging than it used to be because APIs (application programming interfaces) are so popular today. APIs provide a standard way to share the data and services of one system with another, even if the two systems are otherwise incompatible.

According to Gartner, 87% of organizations are classified as having low business intelligence and analytics maturity, meaning they struggle to extract value from their data. This alarming statistic highlights a common struggle—turning raw data into actionable insights. Product teams often find themselves overwhelmed by the sheer volume of information they collect. Extracting meaningful patterns, deciphering user behavior, and predicting market trends from this sea of customer data can seem daunting.

As I eagerly awaited the start of Day 3 at the Global Fintech Fest 2024, the excitement was palpable. The added certainty that our very honorable Prime Minister Shri Narendra Modi would be in attendance boosted that expectation. His attendance was to take the day to another level since everyone eagerly awaited his views on India’s position in the global fintech industry. One could easily note preparations for his arrival were in full swing all over Mumbai.

Large Language Models (LLM) are changing the way developers and QA engineers solve problems. They allow for quicker code generation, debugging, and automated testing, reducing development time by up to 40%. This shift has prompted 67% of senior IT leaders to focus on generative AI, with 33% planning to make it a top priority within the next 18 months. However, while LLM models offer immense potential, understanding how to get the most out of them while maintaining quality is important.

The difference between stateful and stateless applications is essential when developing APIs. However, if you’re not a developer who’s working with stateful and stateless systems, you might not fully understand these concepts. More importantly, you may not know why stateless REST APIs are so essential to building scalable web applications? Here are the key takeaways to know about stateful vs. stateless apps.