Systems | Development | Analytics | API | Testing

The plan is to test earlier and faster to keep up with AI-generated code. By using AI-assisted, in-sprint testing and shift-left strategies, teams can catch issues sooner, scale testing with the same team, and maintain quality despite higher code volume. — Alex Martins, VP of Strategy at Katalon Follow Katalon for more insights in our series!

Testing with production traffic doesn't have to be a security risk. Engineers often avoid production data because of sensitive info like passwords, tokens, and PII. But legacy test data management is too static for modern, fast-changing payloads. Enter the Speedscale Streaming DLP Engine. It automatically detects and redacts sensitive data in real time as it's captured from your environment. You get the realism of production traffic without the risk of a data breach.

Discover the future of the Data Lakehouse with this deep dive into Apache Iceberg V3 and V4 from the Bengaluru community meetup. Learn how PyIceberg and DuckDB are revolutionizing Python-native data processing by eliminating the need for Spark clusters for 99% of common query sizes. Explore high-performance ingestion benchmarks from Oleg and the Google Dataproc Lightning Engine, achieving over 500k rows/sec through Apache Arrow and C++ vectorization. This session is a masterclass for data engineers on metadata compaction, Rest Catalogs, and building vendor-agnostic data platforms.

Leon Taiman, Global Practice Lead at Dell Technologies, discusses the strategic partnership between Dell, Cloudera, and NVIDIA to accelerate the adoption of Private AI on-premises for customers.

The AI Forecast welcomes back John Santaferraro, host of The Digital Analyst Podcast and CEO of Ferraro Consulting, for a candid look at what’s really coming next for AI in 2026.