Systems | Development | Analytics | API | Testing

I’ve always been fascinated by how mobile test automation has evolved. From the early days of scripting interactions in Appium, Espresso, XCUITest, or any other tool, automation has come a long way in validating mobile app functionality. But there’s still one tricky area—visual validation. Functional automation does a great job of checking whether elements exist, buttons are clickable, and text fields accept input.

Functional testing services can increase test coverage and reduce the burden of test creation and maintenance on developers, allowing them to focus on coding new features and updates. However, during calls with prospects, we often hear about challenges with previous service providers. Namely, testing services that blocked releases, overlooked clear user interface (UI) bugs, and introduced new issues, such as poor communication due to language barriers and time zone differences.

You must have heard people say creativity cannot be channelized or forced. It comes from your heart, and there is no process involved. It sounds great when you don't have a lot of clients or a team to take care of. But, if you have them, this can become a horror story. No matter how professional or seasoned you are in your niche, there will be times when clients rip off your work and suggest numerous changes. Without a creative workflow, this can become an endless cycle of revisions.

Rate limiting ensures fair API usage, protects system performance, and prevents resource overload in multi-tenant environments. Here's what you need to know: Pro Tip: Use tools like DreamFactory for built-in rate limiting and traffic management. It simplifies tenant isolation, dynamic adjustments, and monitoring. Rate limiting is essential for fair, secure, and reliable API performance in multi-tenant systems. Dive into the full article for detailed strategies and examples.

Playwright is one of the best solutions if you’re looking for a modern, reliable, and efficient test automation framework. Designed for speed, stability, and ease of use, Playwright helps testers and developers write robust test scripts with minimal effort. Read this Playwright tutorial to learn about this framework, including how to set it up for writing and executing tests. Check the complete Playwright Tutorial video from our workshop.

Exceptions can occur for various reasons, such as invalid input, logical errors, file handling issues, network problems, or other exception conditions. Examples of exceptions in Python include ZeroDivisionError, TypeError, FileNotFoundError, and ValueError, among others. Exception handling in Python is a crucial aspect of writing robust and reliable code in Python.

Data engineering is the backbone of modern analytics, enabling businesses to transform raw data into actionable insights. With the exponential growth of big data, selecting the right tools is crucial for designing efficient, scalable, and reliable data pipelines. This blog explores the best data engineering tools of 2025, highlighting their features, advantages, and use cases to help you make informed decisions.

In today’s rapidly evolving data landscape, functionalities in traditional data warehouses no longer meet the agility, scalability, or performance needs of modern businesses. With cloud-native technologies, real-time analytics demands, and unstructured data sources becoming the norm, organizations are increasingly looking for data warehouse alternatives that are more flexible, cost-effective, and future-ready.

In today's data-driven landscape, efficient data processing is paramount for organizations aiming to extract actionable insights from vast datasets. Databricks, a unified data analytics platform, offers a suite of ETL (Extract, Transform, Load) tools designed to streamline data workflows and enhance analytical capabilities. In this Databricks ETL tools tutorial, we will present the top solutions and how to evaluate them to select the best suit for your use case.