Systems | Development | Analytics | API | Testing

Traffic alone won’t make you sales. You need to understand and direct that traffic down your marketing funnel. But to do that, you need to first know what your traffic comprises of. You need to know who’s coming to you, where they are coming from, what they are doing, and when they are leaving you. A marketing analytics tool helps with just that.

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).

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.

Choosing the right testing framework can feel overwhelming—especially if you're new to Python testing. Pytest and Unittest are both excellent choices, but each shines in different scenarios. Let’s dive into the strengths of each to help you decide which framework suits your project best.

Both Cypress vs Playwright provide powerful solutions for end-to-end testing, but understanding their unique strengths will help you select the best tool for your web application testing needs. Choosing between Cypress and Playwright depends on the specific needs of your project.

Commercial and open source large language models (LLMs) are evolving rapidly, enabling developers to create innovative generative AI-powered business applications. However, transitioning from prototype to production requires integrating accurate, real-time, domain-specific data tailored to your business needs and deploying at scale with robust security measures.

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.

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.