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AnalysisData engineering & the warehouse/lakehouseDatabricks
Databricks Blog · Read source ↗

Databricks is introducing declarative ETL patterns to enhance data warehousing workflows within its Lakehouse platform. This approach aims to streamline the process of transforming and loading data by using higher-level abstractions.

MyDataWork POV — Databricks' push for declarative ETL in the Lakehouse might sound like a leap forward, but it raises significant concerns. By abstracting the complexity of SQL transformations, we risk creating a black box where critical data processes become opaque. This opacity could lead to missteps in data governance and auditing, as the underlying logic becomes hidden from those who need to understand it most. In the quest for simplicity, we must ensure we're not sacrificing transparency and control.
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