9 articles found
SQLMesh’s momentum faded in 2026 while dbt shipped Fusion, swallowed the LLM ecosystem, and tightened its grip. The one feature SQLMesh still dominates might not be enough.
How dbt Labs’ rapidly shifting terminology between ‘Core’, ‘Platform’, ‘Cloud’, and ‘Fusion’ creates real confusion for developers and erodes hard-won trust.
The debate over where business logic belongs in modern data architectures reveals an uncomfortable truth: the ideal of centralizing everything in the transformation layer collides with messy reality. Here’s what actually works.
The unspoken truth about Python packaging in data engineering: sometimes the ‘wrong’ choice is architecturally right. A field guide to knowing when to extract versus when to duplicate.
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As dbt evolves from transformation tool to all-in-one ELT platform with Semantic Models and Fusion, data teams face a critical question: is this convenience worth the ecosystem lock-in?
The debate over nested CTE patterns in dbt models isn’t just about style, it’s about whether modern data warehouses are silently paying for your code’s readability.
The controversial merger between dbt Labs and Fivetran puts open source data tooling’s future in question
The merger between Fivetran and dbt Labs isn’t just consolidation, it’s a strategic power play to flip the data stack hierarchy and make warehouses the commodities, not the kings.
