Because retesting and regression testing have many similarities, it’s easy to get them mixed up. Both are software testing methods used to maintain the usability of a website or web app, and both involve testing your software repeatedly. Thankfully, there are some key differences between the two that are easy to remember when learning how to distinguish one from the other. Read on for a simple breakdown of retesting vs regression testing, so you never end up confusing them again.

One of the largest health insurance providers in the United States uses Unravel to ensure that its business-critical data applications are optimized for performance, reliability, and cost in its development environment—before they go live in production. Data and data-driven statistical analysis have always been at the core of health insurance.

APIs have become increasingly popular in the current SaaS ecosystem due to their ability to seamlessly integrate software systems. APIs provide standardized ways for applications to share data. API monetization is a powerful way for businesses to drive growth and generate revenue from existing API consumer data and usage. By offering your APIs as products or services, your company can tap into new markets, attract more developers, and create self-sustaining ecosystems around your product line.

The exponential growth of the Internet and cloud computing has given rise to applications that are smaller, more distributed, and designed for highly dynamic environments capable of rapidly scaling up or down as needed. These applications have pushed the demand for modern API management product architectures that can leverage cloud native capabilities to achieve scalability, resilience, agility, and cost efficiency.

Microsoft Dynamics 365 is a leading enterprise application suite that empowers businesses to streamline operations, optimize customer engagement, and drive growth. It offers CRM, ERP, human resources, finance, sales, and marketing modules, providing organizations with a unified platform to manage processes and make informed decisions. With its cloud-based architecture, Dynamics 365 offers scalability, flexibility, and seamless integration with other Microsoft solutions like Office 365 and Azure.

Latency is a technical term that, even for the people who use it every day, tends to feel abstract. When a metric calls for measurement by the millisecond, it’s easy to forget just how valuable each millisecond is. But if your application promises to deliver realtime updates – especially in the sports, media, and entertainment industries – low latency has to become a high priority and the experience your users have with latency needs to be vivid.

Rails' date and time helpers are great. They save us from duplicating simple add-duration-to-time logic across our applications and make the code more readable. However, complex date manipulations are dangerous places full of edge-cases. This article discusses some of them.

Application Programming Interfaces (APIs) are everywhere, and with good reason. These bits of software join apps and microservices together despite different programming languages, operating systems, and data formats. An API can integrate services that otherwise can’t communicate with each other. They lay the groundwork for the most successful digital transformation strategies and composable enterprises.

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 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?