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The FITT Way To Data Products:
A New Data Architecture For A Product-Centric World
Transform Your Data Engineer Team from Firefighters to Data Product Builders
Shift your data-architecture-focused thinking to a data-engineering-productivity-focused design. FITT democratizes system ownership, enables junior developers to contribute confidently, and allows senior engineers to concentrate on high-value business logic rather than troubleshooting mysterious failures. Learn why data product teams report their systems becoming "too quiet" (and their customers too happy!)—and why that's exactly what sustainable, scalable data product development looks like.
Stop Fighting Your Data Architecture and Start Building Products That Scale
Are you tired of 3 AM alerts because your medallion architecture's bronze data doesn't match the gold layer? Fed up with spending entire weekends debugging why your pipeline worked yesterday but mysteriously fails today? This webinar unveils the battle-tested FITT (Functional, Idempotent, Tested, Two-stage) data architecture that eliminates endemic burnout, constant firefighting, and hero-driven development that keeps engineers trapped in operational chaos.
Master the FITT Principles That Make Data Engineering "Boring" in the Best Way
You'll learn how to implement functional transformations that produce consistent results every time, build idempotent pipelines where recovery is simple ("Pipeline broke? Just re-run it"), develop comprehensive testing frameworks that identify issues during development instead of in production, and design straightforward two-stage architectures that remove unnecessary complexity from bronze-silver-gold layers.
Get Practical Implementation Techniques That Work in Your Current Stack
Beyond theory, you'll learn specific SQL and ELT strategies, including Functional-Idempotent (FI) chunks that work with modern tools like dbt, Snowflake, and BigQuery. Understand the "complete rebuild from raw" pattern, master parameterization techniques for real environment parity, and apply improved Write-Audit-Publish patterns that ensure reliable data quality without extra operational effort
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