Systems | Development | Analytics | API | Testing

Stories are how humans make sense of complexity. We remember cause and effect, not pie charts. We respond to tension, not tooltips. And yet, in most businesses, “data storytelling” still means downloading a CSV, sending screenshots in Slack, or fumbling through a slide deck minutes before the meeting. The promise was beautiful. Democratize data. Make every decision backed by fact, not gut. Put analytics in the hands of the many, not the few.

BigQuery short query optimizations accelerate small queries by executing them as a single stage. It can significantly improve slot time and latency.

No one starts their work day thinking “Let’s investigate the memory allocations of my Gradle build”, but sometimes life happens (usually at the worst possible time): Every Gradle project faces memory allocation problems eventually, as the codebase grows. So, instead of blindly applying JVM flags from Stack Overflow until it’s resolved, why not take a deeper look?

Manual web testing is still the backbone of quality assurance for many teams. It gives you control, flexibility, and insight that automated tests can’t always match. Before automation tools are introduced, or even alongside them, manual QA testing have helped QA teams identify visual bugs, navigation issues, broken flows, and inconsistencies in real-world user behavior. They are a little bit tedious, but they sure are helpful. This guide will walk you through.

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.