Systems | Development | Analytics | API | Testing

Hey, did you know that Cloudera has achieved the AWS Generative AI Competency? This major specialization milestone continues to validate the technical work we're doing to deliver high-impact Generative AI solutions on AWS. Because AWS Competencies require rigorous technical validation and verified customer success reviews, this achievement proves real-world impact where it matters most.

Traditional test automation is slowing teams down with brittle scripts, constant maintenance, and growing complexity. Perforce Autonomous Testing changes the game with enterprise-grade AI-powered testing that helps teams create, execute, maintain, and analyze tests using natural language—without scripts, frameworks, or ongoing upkeep.

API testing gets harder as it grows: fragmented tools, brittle scripts, thousands of endpoints, and external dependencies that block progress. SmartBear ReadyAPI lets teams ship production-ready APIs faster by validating functionality, performance, and security in one test, with virtualization built in – no separate tools to juggle. In this Demo Den, Thomas Hurley, senior manager of product management at SmartBear, shows how one test does the work of three, so teams cut maintenance time and stop waiting on external dependencies.

Migrating an entire bank's infrastructure without losing a single transaction sounds impossible. Yet, OCBC successfully modernized their event-driven architecture across Singapore, Malaysia, and Hong Kong with zero data loss. In this episode, George Goh (Executive Director at OCBC) joins Joseph Morais and Sami Amed (Staff Solutions Engineer at Confluent) to pull back the curtain on a massive data streaming migration, how the team executed a flawless migration of thousands of producers and consumers in a single weekend.

The SaaS purge is here, and it’s survival of the fittest. On the latest, Joshua McKenzie (@elmosoftware) drops a harsh reality check: If your software isn’t the ultimate "system of record," your customers are already plotting to build over you. If you're just a pretty interface, you're replaceable.

A new Google white paper suggests that as AI writes the majority of our code, the role of a software engineer is shifting from writing applications to performing rigorous validation and QA.

With the Data Product Agent in Qlik, AI can automatically generate curated Data Products using trusted catalog definitions, datasets and metadata. It defines the specification, contract, and reasoning behind the data, while also providing action confirmation before anything is changed.

With Declarative Pipelines in Qlik, teams can use tools like Claude Code, GitHub Copilot, and integration with VS Code, to build and update Qlik Talend Cloud data integration pipelines using natural language prompts.

With the Data Quality Agent in Qlik, AI can guide data engineers through a full dataset quality assessment using natural language prompts. The agent can identify trusted catalog definitions, generate intelligent data quality validation rules, and provide transparent reasoning behind every recommendation.

AI tip: For pull requests, let your AI agent take the first pass before your team ever sees it. That's the workflow Ilia Mogilevsky, Software Engineering Manager at SmartBear, built. By packaging prompts into a skill loaded with Git history, Jira context, and CI checks, he turned a basic AI assistant into a reviewer he trusts. The skill then generates a structured report with approval-ready fixes. Accept the changes, adjust what's off, and push – review cycles shrink and deploys move faster.