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What is the Databricks control plane?
The control plane is the management side of Databricks. Databricks documentation places its backend services and the web application in the control plane, which is managed in the Databricks account. It coordinates the platform; that does not mean customer data processing happens there. Workload execution belongs to the compute plane. Databricks’ high-level architecture documentation describes this distinction for AWS.
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Where does Databricks compute run?
The compute plane is where workloads process data. In the documented AWS architecture, the location and management responsibility differ between classic and serverless compute.
| Dimension | Classic compute (AWS example) | Serverless compute (AWS example) |
|---|---|---|
| Cloud placement | Runs in the customer’s AWS account and network. | Runs in a Databricks-managed compute plane in the same cloud region as the workspace’s classic compute plane. |
| Resource management | The customer provisions and configures resources. | Databricks allocates and manages resources on demand; users do not provision them in their own cloud account. |
| Networking | Can use the customer’s virtual network. | Uses applicable Databricks-managed controls and customer-side access configuration, with workspace network boundaries and isolation controls. |
| Operational trade-off | Offers customer control over resource and network configuration, with corresponding setup and management work. | Databricks says on-demand allocation can speed startup and scaling, reduce idle time, and reduce resource-management work; these are described benefits, not workload guarantees. |
Databricks says serverless compute for notebooks, workflows, and Lakeflow pipelines is available by default in most AWS workspaces. Legacy workspaces without Unity Catalog must upgrade to access it. Other serverless features, including serverless SQL warehouses, have separate configuration paths. Check the current feature-specific requirements and supported data connections before choosing a compute type. The AWS serverless compute documentation covers availability and setup.
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Neither option is automatically cheaper. Compare representative workloads and review actual billing usage. Networking costs and behavior also vary by cloud and region: AWS documentation discusses costs for serverless connections and cross-region egress, while Google Cloud documentation has described different serverless networking charges. Check current documentation for the cloud and region you use rather than applying one provider’s rules to another.
How do networking boundaries fit into the architecture?
For AWS, Databricks’ reference architecture treats these as separate communication paths, each with its own design decisions:
- Users and applications to Databricks: decide how people and client applications access the service.
- Control plane to classic compute plane: plan the back-end connection between platform management and customer-account compute.
- Serverless compute to customer resources or storage: configure how Databricks-managed compute reaches the customer’s resources.
The AWS reference patterns range from managed security through hardened connectivity to isolated environments with private access. The appropriate controls depend on network topology, auditability, and data-exfiltration requirements; serverless connectivity should not be treated as though it uses the same network placement as classic compute. Databricks’ AWS network reference architecture outlines the paths and patterns.
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What does Delta Lake do?
Delta Lake is the table and transaction layer. Databricks describes it as open-source software that extends Parquet data files with a file-based transaction log. The log records committed table versions and determines which data files belong to the current table state. Table metadata also supports schema validation. Together, the protocol and log provide ACID transactions and scalable metadata handling; Delta Lake works with Apache Spark APIs and supports batch and streaming use cases.
In Databricks, Delta is the default format for table operations unless another format is specified. The responsibilities remain distinct: compute executes reads and writes, cloud object storage persists the data files and transaction log, and Delta Lake defines the table’s committed, versioned state. Do not edit table data or transaction-log files directly; Databricks warns that doing so can corrupt tables. See What is Delta Lake in Databricks? and its Delta Lake architecture guidance.
How does Unity Catalog fit into Databricks architecture?
Unity Catalog is the governance and metadata layer for data and AI assets, not a compute engine or storage format. It provides a way to organize assets and manage who can use them. Common governed objects include tables, views, volumes, functions, models, and services, often addressed through the three-level namespace catalog.schema.object.
Databricks describes Unity Catalog capabilities including access controls, lineage tracking, audit logging, discovery, data classification, and governance for AI assets. Managed tables and volumes include Unity Catalog management of the underlying file-storage lifecycle. External tables and volumes receive Unity Catalog governance, but their files remain in separately controlled storage. Details are in What is Unity Catalog?
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A metastore is the regional top-level container for metadata and governance permissions. Databricks’ architecture guidance assigns each workspace to exactly one metastore and recommends one metastore per region as the default operational pattern. Within that regional boundary, catalogs can organize data by domain or environment.
For cross-region sharing, use supported sharing mechanisms. Databricks warns against registering the same shared table as an external table in multiple metastores because metadata and consistency can diverge. See the Unity Catalog architecture guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does this look like in a data pipeline?
A medallion design is one common way to structure data processing, not a required Databricks architecture. It separates data into layers according to how it has been prepared:
- Bronze: preserve incoming source data.
- Silver: clean, validate, and standardize it.
- Gold: prepare business-ready products or aggregates.
Compute runs each transformation. Delta tables can provide transactional storage at every layer, while Unity Catalog can organize and govern the resulting assets and track lineage. Add data-quality checks as records move between layers, and document asset ownership and lineage. Databricks’ Delta Lake architecture guidance and Unity Catalog documentation describe the relevant table and governance capabilities.
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How to choose compute and design the layers
- Start with workload and connection requirements. Confirm that the compute option supports the workload and the data connections it needs.
- Choose a network and responsibility model. Decide whether customer-account classic compute or Databricks-managed serverless compute best fits your security boundaries and operational needs.
- Test the cost for your workload. Benchmark representative jobs and inspect billing usage; do not infer savings from the compute type alone.
- Define table state and governance separately. Use Delta Lake for transactional table behavior and Unity Catalog for asset organization, permissions, and governance.
- Plan regional metadata boundaries. Assign workspaces and catalogs in line with the regional metastore pattern, and choose supported approaches for sharing across regions.
- Review each network path. Evaluate user access, control-plane-to-classic-compute traffic, and serverless access to customer resources as distinct design questions.
Cloud-specific details matter. The placement and networking examples above are based on Databricks’ AWS documentation; do not assume they describe every deployment identically. Verify current workspace eligibility, feature limitations, network controls, and pricing for your cloud and region before implementation.
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