Max Talks with Databand on the Dataset-Centric Approach to Modeling

Max appeared on the MAD Data Podcast, where he discussed a range of topics related to data quality, machine learning, and artificial intelligence. During the two-part episode, Max delved into IBM's acquisition of Databand and the significance of open-source data tooling, alongside exploring the intersection of Data Ops and DevOps. He highlighted the diminishing art of data modeling and analytics and examined the trade-offs between normalizing and denormalizing data. Max also discussed the dataset metaphor as a means to balance human-centered high-level abstractions and computer-optimized lower-level structures, touching on the limitations of semantic layers and the role of Tableau extracts as datasets. Additionally, he explored how Superset supports various approaches, including query-centric, dataset-centric, and semantic-centric methodologies, questioning whether the transform layer adequately captures semantic layer features.

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