Union two or more tables together automatically, AgileData Engineering Pattern #2 - Episode #68
Agile Data N’ InfoShow Notes
Join Shane Gibson and Nigel Vining as they describe and discuss the AgileData Engineering Pattern of Unioning two or more tables together automatically.
The Automated Table Unioning pattern automatically combines two or more tables by intelligently looking up column names and data types to generate safe SQL under the covers. It supports disparate data sources, such as those from multiple publishers in a data clean room, and creates a view or incrementally loads the unified data into a physical table while tracking load watermarks to prevent duplicates.
An AgileData Engineering Pattern is a repeatable, proven approach for solving a common data engineering challenge in a simple, consistent, and scalable way, designed to reduce rework, speed up delivery, and embed quality by default.
If you just want to talk about making magic happen with agile and data you can connect with Shane @shagility on LinkedIn.
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In this episode Shane Gibson sits down with data modeling veteran’s John Giles and Remco Broekmans The conversation explores the premise of democratising data modeling through AI, centered around a paper published by John regarding where AI can and cannot add real value. The group highlights the critical importance of conceptual and context data modeling, stressing that business terms and definitions must be established upfront before looking at physical source tables. They strongly critique the anti-pattern of placing humans “at the end of the line” merely to rubber-stamp automated outputs, arguing instead for an “AI-in-the-loop” approach where technology serves strictly as an assistant under human direction. The discussion also dives deep into complex real-world scenarios where automated tools fail—specifically when physical data or source systems do not yet exist. John shares extreme edge cases, such as establishing a brand-new government department from scratch based on a vague Act of Parliament, unifying over 70 distinct healthcare organisations into a single model, and modeling evolving visions like the Australian wool industry. Remco breaks down his multi-agent ELM framework, explaining how introducing deliberate manual friction points between LLM tasks keeps human modelers in control and why model-storming workshops, human trust, and organisational culture are far more vital than push-button automation. Whether you are curious about conceptual modeling techniques, how to effectively integrate “AI in the loop” while avoiding push-button modeling pitfalls, or strategies for aligning business definitions across complex organisations, this episode is packed with valuable insights for data professionals.
Raw and unedited Top 3 things:
Raw and unedited Top 3 things:
Raw and unedited Top 3 things:
Raw and unedited