Simplify Apache Iceberg Adoption with Python: A Bauplan + Orchestra Demo

Apache Iceberg is rapidly becoming the standard for modern data architectures—but implementing it often means wrestling with Spark clusters, Kubernetes orchestration, catalog management, compaction schedules, and complex distributed tooling. The reality? Many teams face partial migrations, inconsistent table formats, and ballooning infrastructure costs.

In this technical webinar, we demonstrate how Bauplan and Orchestra eliminate that operational overhead. Bauplan delivers a Python-native data plane directly over object storage, enabling you to convert Parquet to Iceberg, create isolated branches for testing, run transformations, and manage tables using a straightforward Python/SQL SDK—no Spark required. Orchestra complements this with enterprise-grade scheduling, monitoring, testing, and observability, all configured through a single YAML file.

We walk through a complete Write–Audit–Publish workflow that transforms object storage into a production-ready, test-gated data platform—without managing clusters or requiring a dedicated platform engineering team.I'll analyze the SRT file to create accurate chapters based on the actual content.Let me search through the full file to identify the key transitions and topics:Let me look at specific timestamp ranges:Perfect! Now let me check for Q&A section:Based on my analysis of the SRT file.