Systems | Development | Analytics | API | Testing

Quality assurance has always been about balance, ensuring every scenario is covered without drowning teams in complexity. Yet, as systems grow and requirements multiply, keeping up with coverage demands has become one of the hardest parts of modern QA. That’s where AI-powered test model generation steps in, not to take over, but to amplify how testers design, visualize, and expand coverage.

Combine your data with generative AI to analyze and improve customer experience.

Long app store queues, slow review cycles, and fragile self-hosted setups have held back mobile teams for too long. CodePush for Bitrise changes that. React Native teams can now deliver updates directly to users in minutes.

The IDC MarketScape: Worldwide Data Integration Software Platform 2025 Vendor Assessment (doc, October 2025) notes, “Qlik delivers a comprehensive, cloud-agnostic data integration platform that spans real-time ingestion and replication, batch ETL/ELT, data quality, data productization, and governed self-service access for analytics and AI.” In the past, data integration was largely about moving information from A to B: ingestion, transformation, loading.

In less than 20 years, mobile apps have grown from a bright new idea to a cornerstone of our daily lives. And while Android accounts for the lion’s share of the market, iOS usage continues to grow exponentially: in 2024 alone, iOS apps and games were downloaded around 35 billion times. The Apple App Store is both a shop window and a marketplace for this global industry. But as iOS developers, how do we actually submit a mobile app to the store?

The new MATCH_RECOGNIZE feature in BigQuery allows you to perform complex pattern matching on your data directly within your SQL queries.

BigQuery managed AI functions let you use generative AI for common analytical tasks within SQL queries, without complex prompt tuning or new tools.

At a previous company, we had over 100 microservices. I’d make what seemed like a simple change to one service and deploy it, only to discover it broke something completely unrelated. A change to the user service would break checkout. An update to notifications would break reporting. We spent more time fixing unexpected bugs than shipping features. The problem was our test scenarios were too simple.