Systems | Development | Analytics | API | Testing

Regression testing is a technical validation performed by QA teams to ensure that code changes haven't broken existing functionality. It asks the question: "Did we break anything that previously worked?" Regression tests run continuously throughout development, often automated within CI/CD pipelines, protecting the application's stability as it evolves. User Acceptance Testing (UAT), on the other hand, is a business validation performed by actual users or stakeholders. It asks.

Modern applications fail in the smallest places – a login field that accepts 257 characters instead of 256, an API that crashes when quantity becomes 0, or a payment system that allows transactions just $1 above the allowed limit. These are not complex logic failures. They are boundary failures.

In the blog post about Fine-Tuning OPcache Configuration I mentioned the thundering herd problem that affects OPcache during cache restarts. When OPcache is restarted, either automatically or manually, all current users will attempt to regenerate the cache entries. Under load this can lead to a burst in CPU usage and significantly slower requests.

Pleo operationalized Customer 360 data in HubSpot without sacrificing governance or accuracy.

How to build an interoperable storage layer on a data lake without ever learning iceberg.

Agentic AI is moving quickly from experiments to real work. In 2026, it shows up inside the workflows that drive outcomes: decisions, operations, and accountability. In the season 7 premiere of the Data Chief podcast, host Cindi Howson sat down with three leaders who work at the intersection of AI ambition and enterprise execution: Paul Baier (GAI Insights), Jennifer Belissent (Snowflake), and Rory Blundell (Gravitee).

For today’s CTOs and CIOs, the pressure to deliver actionable data insights within your products has never been higher. However, a critical dilemma often stalls your progress toward the business intelligence tools you need for the task: Should your engineering team build a bespoke analytics engine from scratch, or should you integrate a professional embedded solution?

Reading emails, scanning contracts, manually processing invoices—the tedious tasks related to document processing can jam up your business operations. Document processing is a prime candidate for automation, but the technology is advancing so fast, it can be hard to know where to start or when it is time to modernize. OCR (Optical Character Recognition) and IDP (Intelligent Document Processing) are two approaches to tackling business documents.

TL;DR: DreamFactory 7.4+ includes a built-in MCP (Model Context Protocol) server that lets you connect any LLM—ChatGPT, Claude, Perplexity, or custom AI agents—to your enterprise databases through governed, role-based APIs. Setup takes minutes: create an MCP service in the admin console, copy the OAuth credentials, and point your AI application to the generated endpoint.

As AI continues to rise in importance across all industries, the cost of implementation, readily available access to cloud computing, and practical business use cases make AI-powered offerings a competitive advantage for product managers, engineering, and data leaders. However, AI isn’t without its fair share of risks and challenges.