Live Query and Designer Desktop Comparison
Live Query and Designer Desktop can both work with data stored in cloud data platforms such as BigQuery, Snowflake, and Databricks. The main difference is where processing occurs and what that means for performance, cost, governance, and data movement.
Designer Desktop performs most standard workflow processing on your computer. Live Query converts supported workflow operations into queries and runs them in the connected cloud data platform.
Compare Processing Environments
Designer Desktop | Live Query | |
|---|---|---|
Processing Environment | Performs most standard workflow processing with the local Designer engine and your computer’s resources. | Runs supported workflow operations directly in BigQuery, Databricks, or Snowflake using cloud data platform compute. |
Data Movement | Reads data from connected sources into Designer when local processing is required. | Converts supported workflow operations into queries that are executed directly in the cloud data platform. |
Performance and Scalability | Depends on factors such as available computer resources, data volume, workflow design, and the selected engine. | Uses scalable cloud compute and is designed for processing large, cloud-resident datasets. |
Governance and Security | Uses the configured connection credentials, along with the local endpoint and organizational security controls. | Uses the existing cloud platform permissions, access controls, and governance policies. |
Compute Usage and Cost | Uses local compute resources for most standard workflow processing. | Uses compute resources from the connected cloud data platform, which might consume platform credits. Consumption can be monitored and managed through the platform’s native controls. |
Design-Time Experience | Requires the user to run the workflow to recalculate and refresh results after changes. Data retrieved from the source is processed locally. | Automatically recalculates and refreshes results after workflow changes, using the full dataset and the cloud data platform compute resources. |
Available Tools | Provides the full Desktop tool palette for a broad range of data preparation, blending, analytics, and specialized processing scenarios. | Provides a pushdown-compatible tool palette. Available tools and configurations may vary by cloud data platform. |
Custom Logic | Supports reusable macros and code-based processing, including Python, for an advanced and specialized workflow logic. | Supports custom SQL through the SQL Transform tool, with execution remaining in the cloud data platform. |
AI Capabilities | Supports AI and advanced analytics capabilities available in the Designer environment. | Can use AI and ML capabilities available natively in the connected cloud data platform. |
Choose Live Query
Use Live Query when:
Your data is stored primarily in BigQuery, Snowflake, or Databricks.
You need to join, filter, or aggregate large cloud-resident tables using scalable platform compute.
Your governance policies restrict downloading or processing data outside the cloud platform infrastructure.
You need to work with the full dataset while designing your workflow.
You want to use custom SQL or AI capabilities available natively in the cloud data platform.
Choose Designer Desktop
Use Designer Desktop when:
Your workflow includes local files or data from multiple types of sources.
You need Designer Desktop-specific capabilities, such as Reporting, Spatial, or Laboratory tools.
You need iterative or programmatic workflow logic, such as iterative macros or Python-based processing.
Your data volumes can be processed effectively using local machine resources.
You want workflow execution to use local compute rather than cloud data platform compute.