databricks Platform · Updated Jul 6, 2026

Materialized View and Streaming Table Refreshes

Now supports standard performance mode for refreshes in jobs.

What it does

The update allows materialized view and streaming table refreshes orchestrated through SQL tasks in Lakeflow jobs to utilize the job's performance optimization settings. When performance optimization is disabled, these refreshes will operate in standard performance mode, which reduces the consumption of Databricks Units (DBUs). This feature enhances the flexibility of scheduling refreshes based on performance needs.

Why it matters

This enhancement is significant for organizations looking to optimize their cloud costs by reducing DBU consumption during less critical refresh operations. It allows data teams to balance performance and cost, ensuring that resources are allocated efficiently while maintaining data freshness.

Key capabilities

Performance Mode Selection

Users can select between performance optimized and standard performance modes for refreshes.

Job Integration

Refreshes can be orchestrated as part of existing Lakeflow jobs.

Cost Efficiency

Standard performance mode consumes fewer DBUs, lowering operational costs.

Flexible Scheduling

Supports various scheduling methods including manual, trigger on update, and time-based schedules.

Recommended use cases

Cost-sensitive environments where DBU consumption needs to be minimized.Scheduled refreshes for data pipelines with varying performance needs.Integrating refresh tasks into complex job orchestration workflows.

Architecture

Lakeflow Jobs → SQL Task → Materialized View → Streaming Table

Get started

Access Job Settings

Navigate to the job settings in the Databricks workspace.

Select Performance Mode

Choose the desired performance mode for the job.

Schedule Refresh

Define the refresh schedule using SQL tasks.

Monitor Refresh Status

Use the UI or SQL commands to track the refresh status.

Adjust as Necessary

Modify the performance mode or schedule based on operational needs.

Key takeaway

The introduction of standard performance mode for refreshes enhances cost efficiency and flexibility in data pipeline management.

2026-07-06-20466c75 Generated by the Databricks Updates Agent · browse all updates