The GenAI revolution is well and truly here. To take inspiration from our favorite comfort show, Gilmore Girls, “It’s GenAI’s world, and we’re just living in it.” In fact, McKinsey reports the number of organizations regularly using GenAI has doubled in ten months between their 2023 and 2024 surveys.

Imagine your development team just rolled out a new feature that fixes a bug but now some parts of your software that used to work flawlessly have stopped working. This is a common risk in Agile development, where changes come fast and often. Since Agile moves quickly with constant updates, regression testing is a key part of making sure the software stays reliable. With each new update, there’s a risk that changes might impact parts of the software that were already working well.

For the ninth consecutive time, Gartner has named Google Cloud Apigee a Leader in the 2024 Gartner Magic Quadrant for API Management.

The Salesforce Data Cloud is a powerful tool that enables businesses to collect, unify, and analyze customer data from multiple sources in real time. As data analysts, the Salesforce Data Cloud provides numerous opportunities for deep insights, better decision-making, and delivering personalized customer experiences.

Your docs are a lot like your family—not in the corporate jargony “we are a family” way, but more in the “can’t live with them, can’t live without them” way. Yes, these docs are crucial in more ways than one, but teams that regularly work with them know that the time they spend searching for, cleansing, and prepping their docs can be better utilized elsewhere.

Security log management provides an organization with a unified and centralized way of collecting, monitoring, and analyzing different security-related activities and events, fostering easier audits and reporting. It offers organizations, the ability to quickly access and analyze logs to understand the scope and impact of security incidents, facilitate regulatory compliance, and enhance visibility and accountability.

The financial document processing domain has undergone a 360-degree shift in the past decade. It was at the brink of the 1980s when software providers began releasing document management systems aimed at helping companies save time, money, and effort in regard to financial document processing. What started as simple document management systems using Optical Character Recognition to digitize printed financial documents has evolved into advanced, AI-powered solutions.

The headless data architecture is the formalization of a data access layer at the center of your organization. Encompassing both streams and tables, it provides consistent data access for both operational and analytical use cases. Streams provide low-latency capabilities to enable timely reactions to events, while tables provide higher-latency but extremely batch-efficient querying capabilities. You simply choose the most relevant processing head for your requirements and plug it into the data.

From artificial intelligence (AI) to machine learning (ML) to conversational chatbots, the tools we use to interact with and consume information are rapidly changing thanks to powerful new technologies that make understanding our data more accessible than ever. One particularly influential field is natural language technology (NLT) and its branches.

Managing issue tracking across teams can be straightforward with centralized platforms like GitHub or BitBucket. But what happens when your testing needs go beyond basic coordination?