Databricks Materialized View Sharing

Databricks · Intermediate ·🌐 Frontend Engineering ·1y ago
Skills: ML Pipelines80%

Key Takeaways

Databricks Materialized View Sharing demonstrates how to share pre-computed query results with Databricks and non-Databricks recipients, reducing computational overhead and improving performance. This is achieved by sharing materialized views (MVs) instead of raw underlying data, ensuring sensitive information remains protected.

Original Description

Watch this demo to see how a data provider can share a materialized view with Databricks and a non-Databricks recipient MVs pre-compute query results, which helps reduce the computational overhead required when providing data to recipients. This directly translates to better performance and lower operational costs. Sharing only the computed MV (as opposed to the raw underlying data) ensures that sensitive or unneeded details from base tables remain protected while still satisfying the recipient’s data needs.
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This demo showcases how to share materialized views with Databricks and non-Databricks recipients, improving performance and reducing operational costs while protecting sensitive data. By pre-computing query results, data providers can satisfy recipient data needs without exposing raw underlying data.

Key Takeaways
  1. Create a materialized view in Databricks
  2. Pre-compute query results for the materialized view
  3. Share the materialized view with Databricks and non-Databricks recipients
  4. Configure data access controls to protect sensitive information
  5. Monitor and optimize materialized view performance
💡 Sharing materialized views instead of raw underlying data ensures sensitive information remains protected while still satisfying recipient data needs.

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