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Databricks separates platform management from workload execution: its control plane manages the workspace and platform, while its compute plane runs data workloads. Delta Lake provides transactional table state over cloud storage, and Unity Catalog organizes and governs data and AI assets. These layers work together, but none is a substitute for another.
What is the Databricks control plane?
The control plane is the management side of Databricks. Databricks documentation describes it as the location for Databricks-managed backend services in the Databricks account; the web application is also part of the control plane. It coordinates the service, while workload processing takes place in the compute plane.
That distinction matters when evaluating data location and security boundaries: control-plane coordination does not mean that customer workload processing happens there.
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The compute plane is where workload processing occurs. Its cloud-account placement and operational responsibilities depend on whether you use classic or serverless compute. The comparison below describes the AWS architecture documented by Databricks; details can differ on other clouds.
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| Aspect | Classic compute | Serverless compute |
|---|---|---|
| Placement in the AWS architecture | 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. |
| Who manages resources | The customer provisions and configures the resources. | Databricks allocates and manages resources on demand. |
| Networking | Can use the customer’s virtual network. | Uses applicable Databricks-managed controls; customer-side access must be configured for connections to customer resources. |
| Operational consideration | Offers customer control over provisioning and network configuration. | Databricks describes potential benefits including faster startup and scaling, less idle time, and reduced resource-management work; these are not guaranteed outcomes. |
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-backed features, such as serverless SQL warehouses, have their own configuration paths. Check current feature-specific limitations and supported data connections before choosing a compute type. Compare representative workloads and billing usage rather than assuming serverless is cheaper.
How should you think about Databricks networking?
In the AWS reference architecture, networking involves three distinct paths, each with its own design questions:
- Users and applications to Databricks: consider how people and services reach the workspace.
- Control plane to classic compute: consider connectivity between Databricks management services and compute in the customer’s AWS environment.
- Serverless compute to customer resources or storage: configure the applicable serverless connectivity and customer-side access controls.
Databricks’ AWS patterns range from managed security through hardened connectivity to isolated environments with private access. The appropriate design depends on network topology, auditability, and data-exfiltration requirements. Do not assume serverless networking follows the same configuration as classic compute. Networking charges and cross-region egress behavior are cloud- and region-specific, so consult current documentation for the deployment in question.
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What does Delta Lake do?
Delta Lake is an open-source table layer that extends Parquet data files with a file-based transaction log. Databricks uses that log to track committed table versions and determine which data files represent the current table state. This protocol supports ACID transactions, scalable metadata handling, and schema validation. Delta Lake is compatible with Apache Spark APIs and supports both batch and streaming workloads.
In Databricks, Delta Lake 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 logs, and Delta Lake’s transaction protocol defines the table’s committed, versioned state.
Use Databricks table operations rather than editing table data or transaction-log files directly. Databricks warns that direct interaction with those files can corrupt a table.
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How does Unity Catalog fit into Databricks architecture?
Unity Catalog is Databricks’ unified governance layer for data and AI assets. It organizes governed objects in a three-level namespace: catalog.schema.object. Common securable objects include tables, views, volumes, functions, models, and services.
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Unity Catalog provides capabilities including access controls, lineage tracking, audit logging, discovery, data classification, and governance for AI assets. It governs how assets are organized, found, and accessed; it is not the compute engine or the physical storage format.
Managed and external assets
For managed tables and volumes, Unity Catalog manages the lifecycle of the underlying file storage. External tables and volumes are governed by Unity Catalog, but their files remain in separately controlled storage. The distinction affects who manages storage lifecycle; governance does not make external files managed storage.
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Metastores and regional organization
A Unity Catalog metastore is a regional top-level container for metadata and governance permissions. Each workspace is assigned to exactly one metastore. Databricks’ architecture guidance recommends one metastore per region as the default operational pattern, with catalogs used to organize data by domain or environment within that regional boundary.
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.
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What does the architecture look like in a data pipeline?
A medallion pattern is one common way to organize data processing, not a required Databricks architecture. A team might preserve incoming source data in a bronze layer, clean and standardize it in silver, then prepare business-ready products or aggregates in gold.
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- Bronze: preserve raw source data for downstream processing.
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Delta tables can provide transactional storage at each layer. Unity Catalog can organize and govern those assets and track lineage. Databricks also recommends data-quality checks as data moves between layers, along with documented lineage and ownership.
How to choose and validate the design
- Choose classic or serverless compute based on workload requirements, supported connections, network posture, and operational ownership—not on an assumed cost or performance outcome.
- Verify workspace eligibility, feature-specific limitations, networking controls, and current cloud- and region-specific pricing before implementation.
- Design the users-to-workspace, control-plane-to-classic-compute, and serverless-to-customer-resource paths separately.
- Use Delta Lake table operations to read and write transactional tables; do not manipulate the underlying data or log files directly.
- Plan Unity Catalog metastores regionally, and use catalogs to organize assets within the regional governance boundary.
For AWS-specific details, see Databricks’ high-level architecture, serverless compute documentation, network reference architecture, Delta Lake overview, Delta Lake architecture guidance, Unity Catalog overview, and Unity Catalog architecture guidance.
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