Systems | Development | Analytics | API | Testing

Qlik Data Products for Analytics is how you turn raw data into something people can actually trust and reuse. It’s built right into Qlik Cloud Analytics and is designed for analytics teams, data producers, and even AI initiatives. Instead of everyone rebuilding datasets over and over, teams can publish curated, governed, analytics-ready data products that include business context, quality checks, and our patented Qlik Trust Score. People discover them in a marketplace, plug them straight into dashboards, apps, or AI workflows, and move fast with confidence. The big value? Less duplication, lower cost, faster app development, and insights you can actually trust.

Cortex Code CLI is expanding capabilities to accelerate your enterprise data lifecycle inside Snowflake! Introducing dbt and Apache Airflow support, expanded model choice across Claude Opus 4.6, Sonnet 4.6, and GBT 5.2. New enterprise-grade governance controls, and a self-serve subscription option. See how Cortex Code CLI helps you ship workflows faster, integrate data systems, and build with confidence using natural language.

Our engineers Ben Boral and Balazs Hajagos have a look at the official release notes to see what's interesting in the latest Xcode beta (already available for use on Bitrise!). Bitrise provides a full-stack, vertically integrated mobile DevOps solution that unites the tools, processes and testing frameworks engineering teams need to build best-in-class mobile experiences. Over 400,000 developers use Bitrise’s products: Bitrise CI, Build Cache, Release Management, and Insights.

In this video, Dipankar breaks down the lakehouse architecture layer by layer - from object storage to table formats, catalogs, and compute engines. He covers.

Retaining total clarity over your metadata doesn't have to be a manual burden. While traditional BI tools often demand more time than they save, this technical walkthrough explores how to leverage CSTools—a Python-based utility—to bypass the UI and fetch metadata directly via REST APIs. Automating your cluster oversight is essential for reliable, code-first tracking of users and groups at scale. By using CSTools, you can eliminate the manual "busy work" and offload the repetitive tasks that weigh down your data team.

See the Databox Model Context Protocol (MCP) in action inside Claude. In this video, we use just three simple prompts to pull Google Business Profile data across 8 different business locations and instantly generate a beautiful, executive-ready HTML dashboard. Instead of manually logging into multiple accounts and copying data into spreadsheets, we use the Databox MCP inside Claude to: Fetch Data Instantly: Claude automatically gathers impressions, clicks, and calls for every single business location through the MCP, comparing current vs. previous month data without any manual exports.

Escaping the monolith didn’t just simplify architecture—it unlocked speed, flexibility, and hiring freedom for Busie.

Teams that get the most out of TrueTest are those handling frequent releases, supporting multiple applications, and testing complex, end-to-end workflows. It is especially valuable for larger testing teams that need to automate at scale, collaborate across members, and avoid maintenance overhead while keeping up with continuous in-sprint testing and regression needs — Alex Martins, VP of Strategy at Katalon.

As telecom operators invest billions in next-generation networks, many are racing to deploy AI to cut costs and unlock new revenue. But beneath the push for automation and smarter infrastructure lies the deeper challenge of rethinking how connectivity itself should be trusted.

Many financial institutions are finding that improving education isn't enough to solve their data management struggles. It’s time to move from “proof of concept” to “intelligence orchestration.” The gap between AI experimentation and real-world ROI is widening. In this video, we break down why a robust, proprietary data foundation is the only way to scale AI safely and effectively. We explore why financial services must move beyond public data and focus on unique, high-value data assets to create a true competitive advantage.