Systems | Development | Analytics | API | Testing

When you’re building something in Python—whether it’s a personal project, an API, or a startup idea—one thing is certain: bugs happen. And while debugging can be fun (sometimes), wouldn’t it be better to catch issues before they cause problems? That’s where testing comes in. In today’s blog, we’ll explore how to test and run your Python applications using Pytest, one of the most popular and beginner-friendly testing tools out there.

In the era of real-time data, Change Data Capture (CDC) in PostgreSQL has become a critical capability for organizations aiming to sync systems, trigger events, and power analytics with fresh, consistent data. This guide will take you through the core concepts, methods, tools, and best practices of how to enable CDC in PostgreSQL instance, making it easier for you to build efficient, reliable, and scalable data pipelines.

In 2025, data doesn’t just support the business — it drives it. That means real-time decision-making is no longer optional. From fraud detection and customer engagement to predictive maintenance and logistics optimization, real-time data processing is the foundation of business agility. Yet many professionals still struggle with legacy bottlenecks: batch ETL jobs, siloed data, and limited pipeline observability.

ETL (Extract, Transform, Load) frameworks have evolved significantly over the past two decades. In 2025, as data pipelines expand across cloud platforms, real-time systems, and regulatory constraints, the architecture and flexibility of ETL frameworks are more critical than ever. This post explores the key principles, features, and operational concerns that modern data professionals need to understand to build effective, scalable ETL frameworks for data engineering use cases.

Testing software is a lot like checking your homework before turning it in. You want to make sure everything works, and you didn’t make any mistakes. In software, there are many kinds of tests, but two of the most common are unit testing and functional testing. Whether you’re a business owner, a developer, a student, or part of a web agency or DevOps team, knowing the difference can help you build better apps, websites, or tools. Is Your Infrastructure Ready for Global Traffic Spikes?

Test automation is an essential component when it comes to delivering high-quality software. However a lot of QA teams face challenges when it comes to deciding which test cases they need to prioritize for automation. The selection process can be hard, especially if you consider the limited resources that these teams sometimes have and complexity of modern applications.

Between the relentless pressure to ship new features and updates, and the increasing adoption rates of DevOps, AI’s influence in the future of the testing landscape is palpable, and the focus in the coming years will be applying that to QA teams everywhere.

When evaluating automation testing companies, you’ll notice most promise the same benefits: faster release cycles, fewer bugs in production, and less testing work for your development team. But in our experience, many fall short. They bottleneck releases, miss bugs users immediately notice, and still rely on your developers to write and maintain tests.

Everyone is taking part in the AI scramble, some more successfully than others, as we all know. But many enterprise companies are still just getting their feet wet. Here at Qlik we saw an opportunity to try to capture what it is that these successful companies did to get value from AI for their organizations. So, we partnered with IDC to get those answers and discovered that preparation is a major difference maker for success with AI.

Data products in BigQuery help teams organize and share data effectively, by managing data as a product, cutting down time to insights.