We will explore the new features introduced in Xcode and review the latest enhancements in testing capabilities

DAGnostics seamlessly integrates Airflow Cluster Policy hooks to enforce governance from local DAG authoring through CI pipelines to production runtime. Learn how it closes validation gaps, collapses feedback loops from hours to seconds, and ensures consistent policies across stages. We examine current runtime-only enforcement and fractured CI checks, then unveil our architecture: a pluggable policy registry via Airflow entry points, local static analysis for pre-commit validation, GitHub Actions CI integration, and runtime hook enforcement. See real-world use cases: alerting standards, resource quotas, naming conventions, and exemption handling. Next, dive into implementation: authoring policies in Python, auto-discovery, cross-environment enforcement, upstream contribution, and testing strategies. We share LinkedIn’s metrics—2,000+ DAG repos, 10,000+ daily executions supporting trunk-based development across isolated teams/use-cases, and 78% fewer runtime violations—and lessons learned scaling policy-as-code at enterprise scale. Leave with a blueprint to adopt DAGnostics and strengthen your Airflow governance while preserving full compatibility with existing systems.

Keynotes & panels featuring industry leaders from Google, AWS, IBM, PayPal, Bank of America, Capital One, Visa, JPMorgan Chase, Hyatt Hotels Corporation, United Nations, Fireworks AI, WaveAI, EA, Dropbox, Ancestry, Oracle, LinkedIn, and more.

LinkedIn Continuous Deployment (LCD), started with the goal of improving the deployment experience and expanding its outreach to all LinkedIn systems. LCD delivers a modern deployment UX and easy-to-customize pipelines which enables all LinkedIn applications to declare their deployment pipelines. LCD’s vision is to automate cluster provisioning, deployments and enable touchless (continuous) deployments while reducing the manual toil involved in deployments. LCD is powered by Airflow to orchestrate its deployment pipelines and automate the validation steps. For our customers Airflow is an implementation detail and we have well abstracted it out with our no-code/low code pipelines. Users describe their pipeline intent (via CLI/UI) and LCD translates the pipeline intent into Airflow DAGs. LCD pipelines are built of steps. Inorder to democratize the adoption of the LCD, we have leveraged K8sPodOperator to run steps inside the pipeline. LCD partner teams expose validation actions as containers, which LCD pipeline runs as steps. At full scale, LCD will have about 10K+ DAGs running in parallel.

This presentation delves into the concept of Swift modularization, highlighting the practice of breaking down monolithic codebases into smaller, more manageable subprojects. We will discuss the various automation tools available that can streamline the creation and management of these subprojects, making the development process more efficient. Additionally, we will explore the benefits of a modularized codebase, such as improved parallel build processes, faster build times, easier maintenance, and enhanced scalability. Attendees will gain practical insights and strategies to optimize their Swift development workflow for increased efficiency and productivity.

My name is Mani, and I'm currently a Staff Engineer at LinkedIn. I have been working on iOS applications for over a decade, focusing on code modularization and reducing build times by simplifying the creation of subprojects.