Adriana Ispas

Fine-grained access control in Unity Catalog isn’t a set of disconnected features; it’s a unified framework. Understanding how permissions, RBAC and ABAC work together is key to governing data and AI at scale. In this session, we’ll break down each approach and when to use it. Learn how granular permissions enforce least privilege, how RBAC creates clear isolation boundaries and how ABAC uses governed tags to apply row filters, column masks and policy logic across entire catalogs automatically. We’ll walk through this as a progression — from dynamic views and table-level controls to scalable, tag-driven policies — so you know how to evolve your approach as your data estate grows. We’ll also cover performance considerations, how to avoid conflicts when combining methods and real-world patterns for production environments. Finally, we’ll preview what’s next, including extending fine-grained governance to AI assets and unstructured data, along with new visibility and impact analysis capabilities in Unity Catalog. Walk away with practical guidance to design, scale and future-proof access control.

We’re introducing a new developer experience for Lakeflow Declarative Pipelines designed for data practitioners who prefer a code-first approach and expect robust developer tooling. The new multi-file editor brings an IDE-like environment to declarative pipeline development, making it easy to structure transformation logic, configure pipelines throughout the development lifecycle and iterate efficiently.Features like contextual data previews and selective table updates enable step-by-step development. UI-driven tools, such as DAG previews and DAG-based actions, enhance productivity for experienced users and provide a bridge for those transitioning to declarative workflows.In this session, we’ll showcase the new editor in action, highlighting how these enhancements simplify declarative coding and improve development for production-ready data pipelines. Whether you’re an experienced developer or new to declarative data engineering, join us to see how Lakeflow Declarative Pipelines can enhance your data practice.

Building robust, production-grade data pipelines goes beyond writing transformation logic — it requires rigorous testing, version control, automated CI/CD workflows and a clear separation between development and production. In this talk, we’ll demonstrate how Lakeflow, paired with Databricks Asset Bundles (DABs), enables Git-based workflows, automated deployments and comprehensive testing for data engineering projects. We’ll share best practices for unit testing, CI/CD automation, data quality monitoring and environment-specific configurations. Additionally, we’ll explore observability techniques and performance tuning to ensure your pipelines are scalable, maintainable and production-ready.