Systems | Development | Analytics | API | Testing

React Hooks changed the way we compose components, but testing them can be a mess—especially if you’re using asynchronous data, having side effects that require cleanup logic, or need to check dependencies from React Context. The react-hooks-testing-library (deprecated now in favor of testing-library/react) solves all of these problems, allowing developers to test hooks in isolation, configured for specific behavior.

As software systems become distributed, scalable, complex and dynamic, observability tools become essential to ensure smooth functioning of the systems, early detection of the issues and quick resolution. Sentry and Datadog are two main observability tools which is used in the modern DevOps workflow. Let’s see the detailed comparison of Datadog and Sentry in this blog.

In this tutorial, we will build a Flask app with Docker, create a background worker with Celery, and use RabbitMQ as the message broker between the two services. This (relatively) simple example will be used to demonstrate how Docker makes it easier to run multiple services and share configuration details between developers. In the last section, we will also build and optimize our app for deployment to Render, so we can avoid some of the common gotchas it is easy to fall victim to along the way!

AI coding assistants are transforming the way developers approach software development by automating routine tasks and enhancing code quality. These tools leverage artificial intelligence and machine learning to provide real-time code suggestions, auto-complete functions, and even debug existing code, making the development process faster and more accurate.

Data warehouses have long been the gold standard for data analysis and reporting. Developed in the 1980s, the data warehouse concept was introduced to provide an architectural model for data flows from operational systems to decision support systems.

For data engineers and architects evaluating Spanner ETL solutions, the landscape has become more complex. Organizations must balance the need for sophisticated data transformations with accessibility for non-technical users, all while managing Spanner's unique architectural requirements. The right ETL tool can mean the difference between a successful implementation that delivers on Spanner's promise of global scale and consistency, or a costly project that fails to meet performance expectations.

Our Advanced CI team is always trying to push what we have further, which often means we have to support APIs and code that are not yet fully stable. On the other hand, we are bound to support projects that are using deprecated code to a certain extent. This is particularly true for build systems that are rapidly changing to support the needs of application developers, such as Gradle.

Large Language Models (LLMs) like ChatGPT and Claude have revolutionized how we think about business automation and conversational interfaces. So it’s no surprise that many organizations are considering building their own LLM-powered chatbot. But here’s the truth: creating a secure, scalable, and intelligent chatbot from scratch is harder than it looks.

Right now, the insurance industry faces an urgent cybersecurity threat: Scattered Spider. The financially motivated hacking group has rapidly shifted its focus after preying on retail companies in the U.K. and U.S. Now, it is targeting insurance companies. The danger is clear. Insurance firms manage exactly what cybercriminals want: vast amounts of sensitive customer data.

You just landed your biggest deal ever. An enterprise client is ready to sign a million-dollar deal, but there’s one non-negotiable: they need your SOC 2 report in 90 days. Now you’re in trouble. Internal controls, access policies, logging, vendor due diligence it all hits at once. The team is Googling terms like “SOC 2 Type II” and “audit readiness,” trying to make sense of what feels like a regulatory jungle. This happens every day in fast-growing SaaS companies.