Table of Contents
Introduction
For clients planning to implement Databricks in the cloud, they need to make the right choice among AWS, Azure, or GCP. AWS and Azure are the most popular choices for running Databricks, and both provide the services required to support data engineering, analytics, and AI workloads. But these two cloud service providers differ in the features and architectural options available for data storage, identity and access management, networking, security, analytics, and AI.
So, enterprises need to understand the differences between AWS and Azure for Databricks deployment to establish the right foundation for their data and AI/ML architecture. Their choice can influence how their data gets stored and accessed, how security and governance will be implemented, which native cloud services can be integrated, and how workloads get designed and operated.
Bacancy Technology brings more than a decade of experience in cloud and data engineering, with certified Databricks professionals working across AWS, Azure, and other key cloud platforms. Our experience delivering Databricks solutions across both environments gives us a good understanding of the key considerations involved in each approach and helps us guide enterprises in making the right choice.
Top 6 Points of Difference Between AWS vs Azure for Databricks
Here’s a detailed overview of the six key differences between AWS and Azure for running Databricks.
| Point of Difference | AWS | Azure |
| Cloud Ecosystem | Databricks integrates with AWS infrastructure and services such as S3, IAM, and VPC | Azure Databricks integrates with Azure services such as ADLS Gen2, Entra ID, and Azure AI |
| Data Storage | Amazon S3 for Delta Lake tables, raw data, and other data assets | ADLS Gen2 for Delta Lake tables, raw data, and other data assets |
| Identity & Security | IAM, VPC, KMS, and Databricks access controls | Entra ID, VNet, Key Vault, and Databricks access controls |
| Data & Analytics Ecosystem | Glue, Redshift, Athena, S3, Power BI, and Tableau | Fabric, Power BI, Data Factory, Purview, and Azure services |
| Ease of Use | Familiar environment for teams already working with AWS services and infrastructure | Familiar environment for teams already using Azure and Microsoft technologies |
| Pricing Structure | DBUs plus AWS infrastructure costs, with options such as Spot and Reserved Instances | DBUs plus Azure infrastructure costs, with options such as Spot VMs and Reservations |
- Cloud Ecosystem and Integration
Databricks is available on both AWS and Azure, but the deployment experience differs in how Databricks integrates with the cloud provider’s infrastructure and native services.
On AWS
On AWS, Databricks is provided as the Databricks platform within the AWS cloud environment and comes with integration support for AWS native services such as Amazon S3 for storage and AWS identity, networking, and security services as part of the surrounding architecture.
Teams can continue using their existing AWS infrastructure and operating practices while introducing Databricks as part of the data and AI platform.
On Azure
Azure Databricks is a first-party Azure service developed through the partnership between Microsoft and Databricks. It brings the Databricks platform into the Azure ecosystem with integrations across Microsoft services such as Microsoft Entra ID, Azure Data Lake Storage, Power BI, and Azure AI services.
This makes Azure Databricks particularly relevant for organizations that want their Databricks environment to fit closely into an existing Microsoft cloud and data architecture.
2. Data Storage
Where an organization’s data is already stored can be an important factor when choosing between AWS and Azure for Databricks. AWS commonly uses Amazon S3, while Azure uses Azure Data Lake Storage Gen2 (ADLS Gen2).
AWS Storage
Databricks on AWS commonly uses Amazon S3 to store lakehouse data, including Delta Lake tables, raw data, and other data assets. Organizations can continue using S3 as part of their Databricks environment without moving their existing data to a different storage service.
For companies that already have most of their data in S3, it makes sense to choose AWS for Databricks. Their existing storage setup can remain in place while Databricks is added to the data platform.
Azure Storage
Azure Databricks commonly uses Azure Data Lake Storage Gen2 (ADLS Gen2) for lakehouse data. ADLS Gen2 supports Delta Lake workloads and works with the wider Azure data platform.
For organizations that already store their data in ADLS Gen2, keeping Databricks within Azure can avoid moving large volumes of data simply to support the new platform.
3. Identity & Security
Identity and security are another area where the existing cloud environment can influence a Databricks deployment. Both AWS and Azure provide the controls needed to manage access, networking, encryption, and security, but the services and identity frameworks used around Databricks differ.
AWS Security Model
Databricks can work with AWS identity and security services as part of the organization’s existing security setup. AWS IAM, VPC, AWS Key Management Service, and other controls can be used alongside Databricks access controls to manage user access, network connectivity, and encryption.
Organizations that already use a centralized identity provider can also connect it with their Databricks environment. This allows the Databricks security model to remain aligned with the identity and access practices already used across the AWS environment.
Azure Security Model
Azure Databricks integrates closely with Microsoft Entra ID and other Azure security services. Organizations can use Entra ID for identity and access management while applying Azure networking, Key Vault, private connectivity, and other security controls around the Databricks environment.
This can be particularly useful for organizations that already manage their cloud access and security through Microsoft. Databricks can be incorporated into the existing identity and security model rather than introducing a separate approach for the data platform.
4. Data & Analytics Ecosystem
The data and analytics tools already used by an organization can influence how Databricks fits into the wider data platform. Both AWS and Azure support Databricks alongside other data and analytics services, but their surrounding ecosystems offer different integration options.
AWS Data & Analytics Ecosystem
Databricks can work alongside AWS data services such as AWS Glue, Amazon Redshift, Amazon Athena, and Amazon S3. Organizations can also connect Databricks with external BI and analytics platforms such as Power BI and Tableau.
This gives AWS organizations flexibility in how they combine Databricks with their existing data and analytics tools. Databricks can become part of the current data platform without requiring the organization to standardize on a single analytics ecosystem.
Azure Data & Analytics Ecosystem
Azure Databricks has closer alignment with Microsoft’s data and analytics services. Organizations can connect it with Microsoft Fabric, Power BI, Azure Data Factory, Microsoft Purview, and other services across the Microsoft ecosystem.
This can be useful for organizations that already rely on Microsoft for data integration, reporting, and analytics. Databricks can work alongside these services as part of the existing Microsoft data environment.
5. Ease of Use
The core Databricks experience is broadly consistent across AWS and Azure. The difference in day-to-day ease of use often comes from the cloud environment around Databricks and how familiar the organization’s teams are with it.
AWS Experience
For teams already working extensively with AWS, using Databricks within the AWS environment can feel more familiar. Existing knowledge of AWS services, access management, networking, and infrastructure can reduce the learning required to manage the wider environment around Databricks.
Azure Experience
Azure Databricks can offer a similar advantage for teams that already work with Microsoft technologies. Familiarity with Azure services, Microsoft Entra ID, Power BI, and other Microsoft tools can make it easier for teams to incorporate Databricks into their existing workflows.
6. Pricing Structure
Databricks pricing is based on usage, with Databricks Units (DBUs) forming a key part of the cost calculation. The final spend also depends on the cloud resources used to run workloads, so compute pricing and available cloud discounts can make a meaningful difference between AWS and Azure.
With AWS
Databricks usage on AWS is charged through DBUs, while the AWS infrastructure supporting the workloads adds its own costs. Organizations can use AWS Spot Instances for eligible workloads where interruptions are acceptable, helping reduce compute costs.
AWS also provides options such as Reserved Instances for longer-term compute requirements, while tools such as AWS Cost Explorer and AWS Budgets can help teams monitor and manage cloud spending.
With Azure
Azure Databricks also uses DBU-based pricing, with Azure infrastructure costs added based on the resources supporting the workloads. Organizations can use Azure Spot VMs for suitable workloads and Azure Reserved Instances to reduce costs for predictable, long-term compute usage.
For organizations already committed to Microsoft Azure, existing Azure agreements and purchasing arrangements can also factor into the overall cost of running Databricks.
Bacancy Technology’s Verdict on Making the Right Choice
After working with Databricks across AWS and Azure, one thing has become clear to us: the cloud decision is often made before Databricks enters the picture. The existing cloud architecture, where the data is stored, which identity and security controls are already in place, and which analytics and AI services the organization has adopted can have a greater impact on the decision than the differences between the two Databricks environments.
For an organization deeply invested in AWS, choosing Databricks on AWS can preserve the existing architecture around S3, IAM, VPC, and AWS services. For an organization with a strong Microsoft footprint, Azure Databricks can provide closer alignment with ADLS Gen2, Microsoft Entra ID, Power BI, Fabric, and Azure AI services.
The decision becomes different when an organization is starting without a strong preference for either cloud. In that situation, Bacancy Technology’s Databricks consultants assess the expected Databricks workloads, data requirements, security model, team capabilities, cloud service requirements, and projected costs across both environments before recommending an option.
Author bio
Chandresh Patel is a seasoned technology professional and passionate writer at Bacancy Technology, covering software development end-to-end, from architecture and cloud infrastructure to data engineering, DevOps, product delivery, and applied AI. He writes for engineering and product teams across industries, with recurring work in regulated sectors such as healthcare and Fintech. He also mentors engineers on Agile delivery practices.