Databricks(@databricks):More ways to build, validate, and manage data preparation workflows in Lakeflow Designer 🫡 Our October roundup highlights the latest enhancements: • Guardrails for data quality checks, with control over what happens when checks fail • No-code operators for cleaning and deduplication • Multi-table joins in a single operator • Parameterized inputs and outputs • Visual version history to easily review workflow changes

Databricks

@databricks

Databricks is the Data + AI company, helping organizations build and scale data and AI apps, analytics and agents.

加入 July 2013

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Databricks@databricks

2026.11.01 15:45

More ways to build, validate, and manage data preparation workflows in Lakeflow Designer 🫡 Our October roundup highlights the latest enhancements: • Guardrails for data quality checks, with control over what happens when checks fail • No-code operators for cleaning and deduplication • Multi-table joins in a single operator • Parameterized inputs and outputs • Visual version history to easily review workflow changes

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