Data Plane
An Ascend Data Plane is an external data warehouse or lakehouse where Ascend persists data. It provides both storage (where your tables live) and compute (where your queries run) for a Flow. A Flow runs on one Data Plane, but a Project can have many Flows running on many Data Planes, enabling a data mesh architecture.
What the Data Plane does
- Storage: Persists all data from your Flow's Components as tables or views
- Compute: Executes SQL and Python code for data processing
- Data ownership: Your data lives in external infrastructure, not in Ascend
Pushdown execution
Ascend acts as an orchestration and metadata layer on top of your Data Plane. When you build a pipeline, Ascend generates and optimizes queries. Your Data Plane executes those queries using its compute resources and stores the resulting tables. Ascend tracks metadata, lineage, and orchestration state.
This architecture is called pushdown execution — Ascend pushes all data processing down to your Data Plane's native engine for maximum performance.
DuckDB with DuckLake keeps compute within Ascend Flow runners rather than pushing down.
What the Data Plane does not do
- Orchestration: Ascend handles scheduling, dependencies, and workflow coordination
- Metadata management: Ascend tracks lineage, schemas, and data quality
- Observability: Ascend monitors Deployments and tracks pipeline performance
Supported Data Planes
- BigQuery
- Databricks
- DuckDB with DuckLake (Ascend-managed)
- Snowflake
External vs. Ascend-managed
External Data Planes (BigQuery, Databricks, Snowflake): You own and manage the infrastructure. Ascend connects via secure credentials and executes operations on your behalf.
DuckDB with DuckLake: Ascend's fully managed option. No setup required, billing included in your Ascend plan. Ideal for use cases without long-term data retention needs.