Databricks on AWS vs Azure vs GCP: Choosing Your Cloud

Databricks runs on AWS, Azure, and Google Cloud with a consistent core experience, the same lakehouse, Spark runtime, Delta Lake, and Unity Catalog, while differing in object storage, identity, networking, and procurement. Pick the cloud where your data, identity, and skills already live; the Databricks layer behaves nearly identically across all three. After reading, you will know what stays the same, what changes per cloud, and how to choose confidently.

• Compare the cloud-specific differences in storage, identity, and networking
• Understand what remains consistent across AWS, Azure, and GCP deployments
• Apply a decision framework to select the right cloud for your workloads

Who this is for: Architects and platform owners selecting a cloud for a new Databricks deployment.

Part of the What is Databricks section in the Databricks tutorial series.

Architecture / Concept Overview: Databricks on AWS vs Azure vs GCP: Choosing Your Cloud

Databricks layers the same platform on each cloud's primitives. The lakehouse, compute model, and governance are constant; what changes is the underlying object store (S3, ADLS, or GCS), the identity provider, the networking constructs, and how you buy the service. This consistency means skills and code transfer across clouds with minimal change.

*The Databricks platform and Unity Catalog stay consistent while integrating each cloud's storage, identity, and networking primitives.*

A short decision flow helps narrow the choice quickly based on your existing footprint.

*A simple decision flow: follow your existing data gravity, identity provider, and team skills to the matching cloud.*

Key Terms

Object storage

The cloud's durable, scalable file storage that backs Delta Lake: Amazon S3, Azure Data Lake Storage (ADLS), or Google Cloud Storage (GCS).

Identity provider

The system managing users and roles: AWS IAM, Microsoft Entra ID (formerly Azure AD), or Google Cloud IAM.

Private networking

Cloud constructs (VPC/VNet, private endpoints) that keep traffic off the public internet.

Marketplace/first-party

How you procure Databricks: a first-party Azure service, or marketplace/account-based setups on AWS and GCP.

Region

The geographic location where your workspace, compute, and storage reside, affecting latency, residency, and feature availability.

Data gravity

The tendency for compute and services to gather where large datasets already live, a key driver of cloud choice.

Prerequisites and Setup

• An account with at least one target cloud provider and rights to create resources
• Knowledge of your organization's identity provider and networking standards
• A target region that meets data-residency requirements
• Familiarity with the cloud's object storage service

Step-by-Step Implementation

1. Confirm regional availability

   Check that Databricks and the features you need are offered in your required region before committing.

   # bash cell - list workspaces / regions you already manage\ndatabricks workspaces list

2. Provision cloud storage for the data layer

   Create the object storage location Databricks will use; the command differs per cloud but the concept is identical.

   # bash cell - examples per cloud (run the one for your provider)\naws s3 mb s3://my-lakehouse-data            # AWS\naz storage account create -n mylakehouse -g rg --sku Standard_LRS  # Azure\ngcloud storage buckets create gs://my-lakehouse-data  # GCP

3. Connect identity

   Integrate your identity provider so users and groups sync into Databricks for governance.

   -- SQL cell - grant to a synced identity group (cloud-agnostic in Databricks)\nGRANT USE CATALOG ON CATALOG main TO `data-platform-team`;

4. Configure storage credentials in Unity Catalog

   Register an external location so Databricks can govern access to your bucket/container uniformly.

   -- SQL cell - external location concept is the same across clouds\nCREATE EXTERNAL LOCATION lakehouse_data\nURL 'abfss://data@mylakehouse.dfs.core.windows.net/'\nWITH (STORAGE CREDENTIAL my_cred);

5. Validate a portable workload

   Run the same Spark/SQL code you would run on any cloud to confirm parity.

   # Python cell - identical code regardless of cloud provider\nspark.range(1000).write.format("delta").mode("overwrite").saveAsTable("main.demo.numbers")\nprint(spark.table("main.demo.numbers").count())

Configuration Reference

Monitoring, Cost, and Security Considerations

Monitoring

Databricks system tables and query/job history work the same across clouds, so your observability dashboards are portable. Each cloud also surfaces its own infrastructure metrics, which you can correlate with Databricks DBU usage for full-stack visibility.

Cost Optimisation

DBU rates and instance pricing vary by cloud and region, so model cost against your actual instance types and commit/discount options. Co-locating compute and storage in the same region avoids cross-region data transfer charges, which can otherwise add a meaningful percentage to spend.

Security and Governance

Unity Catalog provides a consistent governance model regardless of cloud, while each provider supplies the encryption, networking, and key-management primitives underneath. Align with your existing identity provider to avoid duplicating user management.

Common Pitfalls and Recommended Patterns

• Choosing on price alone: data gravity and identity usually matter more than headline DBU rates.
• Cross-region setups: keep compute and storage in the same region to avoid latency and egress fees.
• Duplicating identity: integrate the cloud's existing identity provider instead of separate accounts.
• Ignoring residency: confirm the region meets compliance before building.
• Assuming feature parity by date: verify newer features are available in your specific region.

Frequently Asked Questions

Related Topics

• The Databricks Data Intelligence Platform Explained — The platform that runs consistently on each cloud.
• Core Components of the Databricks Platform — Components that map onto each cloud's primitives.
• Databricks Pricing and DBU (Databricks Unit) Explained — How pricing varies across clouds.
• What is a Data Lakehouse? — The storage architecture backed by each cloud's object store.