In our data-driven world, the landscape of product analytics is rapidly evolving. With the rise of Artificial Intelligence (AI) and Machine Learning (ML), we're seeing a seismic shift in how businesses approach product development and enhancement. But how does AI and ML fit into product analytics, particularly for non-technical business leaders and marketers? And more importantly, what does this mean for the future?

I am excited to share that Tricentis has acquired Waldo, a mobile test automation platform. Waldo complements and extends our existing Tricentis mobile testing offerings with new test automation capabilities that will allow our customers to deliver higher quality mobile applications.

A few weeks ago, I wrote a post summarizing "Seven Data Integration and Quality Scenarios for Qlik | Talend," but ever since, folks have asked if I could explain a little deeper. I'm always happy to oblige my reader (you know who you are), so let's start with the first scenario: Database-to-database synchronization.

C# is a widely used programming language in enterprises, especially for those that are heavily Microsoft-dependent. This language comprises a lot of tools with individual strengths. Here, we list C# tools for IDEs, profilers, automation tools, and more. If you build apps using C#, you most likely use Visual Studio and have explored some of its extensions to supercharge your development. However, this list of C# tools might just change the way you write C# code for good.

An overview of common low-hanging fruits to help you get started with machine learning.

According to Harvard Business Review – Only 3% of Companies’ Data Meets Basic Quality Standards. In the world of data integration, Extract, Transform, and Load (ETL) processes play a vital role in seamlessly moving and transforming data from diverse sources to target systems. However, ensuring the quality and integrity of this data is crucial for accurate decision-making and business success. ETL testing is the key to achieving reliable data pipelines.

In this Kong Konnect tutorial, you’ll learn how to leverage the platform to manage your API ecosystem from a single easy-to-use interface.

Application Programming Interface, or API, development is a critical aspect of modern software engineering, enabling diverse software systems to interact and share data seamlessly. The API serves as a contract between different software applications, outlining how they can communicate with one another. Two of the most popular architectural styles for building Web APIs are Representational State Transfer (REST) and GraphQL.

How easy would our lives be, as programmers, if our applications behaved correctly 100% of the time? Unfortunately, that’s not the case: things go wrong, often in serious ways. In order to offer the user the best possible experience—and also understand what went wrong so you can fix it—your application needs to handle its errors, and that’s why error handling is an important part of any application.

The success of businesses like Zoom, DropBox, and Slack demonstrates the power of product-led growth (PLG) as a strategy for scaling software companies in 2023. Central to this approach is event analytics, the practice of analyzing event data from a software product to unlock data-driven insights. Companies following a PLG strategy (“PLG companies”) use this data to inform product development decisions to enhance user experiences and drive revenue.