Systems | Development | Analytics | API | Testing

Automation testing has become an essential part of modern software delivery. It helps teams release faster while ensuring quality at every stage of development. A well-defined automation test strategy gives QA teams a roadmap to plan, build, and execute automated tests effectively. Behavior-driven development (BDD) takes this further. It bridges the gap between business stakeholders, developers, and testers by using plain language scenarios.

Batch testing is the process of executing a group of test cases or scripts together without manual intervention. It is one of the most effective ways to validate multiple features at once, especially during regression testing or nightly builds. Instead of running each test manually, you can automate the batch testing process to save time and reduce repetitive work. This approach is widely used in QA teams to improve test execution speed and catch defects early in the software development lifecycle.

Are you a QA tester grappling with the challenge of making sure geolocation testing is reliable and efficient? If so, then you’re in the right place. Geolocation testing is an important process for anyone dealing with applications or services that rely on location information from users around the world.

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!

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.

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.

Choosing the right IoT protocol for edge microservices can save energy, reduce latency, and improve efficiency. Here's a quick guide: MQTT: Lightweight and energy-efficient, ideal for resource-constrained devices. AMQP: Reliable and secure, great for mission-critical applications but resource-intensive. CoAP: Best for low-power, low-bandwidth devices with minimal energy needs. HTTP/REST: Widely compatible but consumes more energy and bandwidth.

Marketing should be the hub of go-to-market strategy – not the internal agency that just says “yes” to everything. But what does it take to actually shift that perception and function?

It's not even 9 AM, and the day's already started. A customer can't reset their password. The product team just found a bug in the app. A partner bank needs data synced ASAP. Oh, and the CEO wants to know when the mobile app can do instant loans. Sounds about right? Yeah, for banking leaders, this is just another Tuesday. Customers want everything to be easy and digital. Stakeholders want things done well, safely, and by the book. And nobody has time for old-school stuff.

Modern data architectures rely on purpose-built analytics data platforms, stakeholder buy-in, and flexible, automated data integration.