Introducing the Databricks Connector for Google Sheets: Real-Time, Governed Lakehouse Data in the Sheets Users Love

· Source: Databricks · Field: Technology & Digital — Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Databricks has released a new connector for Google Sheets, designed to bridge the gap between business users operating in spreadsheets and the governed data residing in lakehouses. This connector integrates live Databricks data directly into Google Sheets, leveraging the performance and security features of Databricks SQL and Unity Catalog. Previously, connecting governed data to Sheets involved CSV exports, snapshots, or custom pipelines, leading to data inconsistencies, wasted analyst time, and governance challenges. The new solution allows users to query Unity Catalog-governed datasets directly from Sheets using either a no-code GUI or SQL, with permissions automatically managed by Unity Catalog. Users can also schedule or manually refresh data to ensure their spreadsheets always reflect the latest information, enabling data leaders to maintain governance while empowering business users.

Key takeaway

For data leaders aiming to democratize data access without compromising governance, the Databricks connector for Google Sheets offers a direct solution. You can empower business users to analyze live, governed data within their familiar spreadsheet environment, reducing reliance on manual exports and ensuring consistent metrics across the organization. Implement this connector to streamline data workflows and enhance decision-making speed.

Key insights

The Databricks connector brings live, governed lakehouse data directly into Google Sheets, eliminating data silos.

Principles

Method

Connect Google Sheets directly to Databricks SQL, query Unity Catalog-governed data via GUI or SQL, and refresh results on demand or on schedule.

In practice

Topics

Best for: Executive, Data Analyst, Data Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.