Throughout the evolution of technology, data has become the backbone of business innovation and strategic evolution. It's no surprise that the architects behind these massive data structures, known as Data Integration Architects, have been the unsung heroes in this transformation. As orchestrators of vast data landscapes, they not only ensure data cohesiveness but also bridge the gap between raw data and actionable insights.

“It’s no use! I can’t run an end to end test with Flutter’s integration tests”, exclaimed one of our customers about 9 months ago. I asked what the problem was and they explained that they were using Google Authentication for logging in and used the google_sign_in package for and it wasn’t possible use Flutter’s integration tests to interact with the login screens.

When running automated tests frequently on your website, at one point it may be essential to keep your website statistics consistent with correct visitor counts, conversions, and geo-location data. The impact of such skewed data from automation can lead to pricy mistakes for incorrect ad targeting and the business economy statistics, hence it can be important to exclude test automation from analytics data.

We are thrilled to share that we’ve raised $7M in seed funding! At Koyeb, we simplify app deployment with our global serverless platform. We provide an easy way to deploy full-stack applications and databases in production, everywhere, in minutes. We’re focused on allowing developers and businesses to seamlessly build, run, and scale any application globally, with no code rewrite or infrastructure management.

Exploratory testing is a dynamic, flexible methodology emphasizing simultaneous learning, testing strategy, and execution. Unlike traditional scripted testing, exploratory testing enables testers to actively explore software applications using their intuition, creativity, and experience. By assuming the end-user role, testers interact with the software in real-time, identifying potential issues and uncovering usability problems that scripted tests might overlook.

As the role of APIs continues to expand, the way we manage them is undergoing a profound transformation of its own. Visitors to the Gartner rating and review site may have noticed a significant shift in the terminology used to describe API management.

Snowflake’s single, cross-cloud governance model has always been a powerful differentiator, enabling customers to manage their increasingly complex data ecosystems with simplicity and ease. As a result, Snowflake is enhancing its governance capabilities that thousands of customers already rely on through Snowflake Horizon. Snowflake Horizon is Snowflake’s built-in governance solution with a unified set of compliance, security, privacy, interoperability, and access capabilities.

Imagine looking at a bland spreadsheet filled with hundreds of columns containing nothing but some raw numbers… Be honest – how well would you understand the data presented in front of you? Even if you could, it’ll probably take some time until you connect the dots of how everything relates to one another. And on another note, not everyone in your business will be as data-savvy as you are. So, how can we fix this and make data understandable to all of the key members?

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse, data lake and data lakehouse, and distributed patterns such as data mesh. Each of these architectures has its own unique strengths and tradeoffs.