Many organizations today run data lakehouses across AWS, on-premises systems, or other cloud platforms. As business needs evolve—whether through acquisitions, regional expansion, or specific application requirements—you may need to incorporate Azure into your existing data infrastructure. The critical question becomes: can you extend your current lakehouse architecture to Azure without disrupting your established workflows?
The answer is yes, but success depends on choosing a lakehouse solution designed for true multi-cloud flexibility. This guide explores how to thoughtfully add Azure to your data lakehouse strategy while maintaining the consistency and reliability your teams depend on. Importantly, all of this is possible without embarking on a plan of complete data centralization.
Whether you’re running primarily on AWS with some Azure workloads, operating on-premises with Azure for specific applications, or managing a hybrid environment, the principles remain the same: your lakehouse should work seamlessly across all platforms without requiring separate architectures or creating operational complexity.
The challenge of extending to Azure
When you need to add Azure to an existing data infrastructure, several concerns naturally arise:
- Will adding another platform fragment your analytics capabilities, forcing teams to learn new tools and maintain separate workflows?
- How do you avoid the expense and complexity of duplicating your entire data infrastructure on Azure?
- Can you maintain consistent security policies and governance across your expanded environment?
- Will Azure require different operational procedures or specialized expertise?
The traditional approach—building a separate Azure-specific data architecture—creates exactly the problems you’re trying to avoid. Your teams end up managing multiple systems, data gets copied between platforms at significant cost, and maintaining consistent governance becomes a coordination nightmare. This fragmentation undermines the very benefits that drove you to adopt a lakehouse architecture in the first place.
Understanding the data lakehouse approach
A data lakehouse combines the capabilities of data lakes and data warehouses. It provides warehouse-like performance and governance on cost-effective lake storage.
The lakehouse architecture supports all data types—structured, semi-structured, and unstructured—while maintaining ACID transactions for data reliability. It delivers strong security and governance controls alongside schema evolution capabilities that adapt as business needs change. Most importantly, it provides fast query performance for interactive analytics without sacrificing the cost advantages of lake storage.
When built on open table formats like Apache Iceberg, a lakehouse enables different engines and tools to access the same data without costly ETL or data duplication.
When Azure becomes part of your data landscape
Organizations typically need to incorporate Azure into their existing lakehouse for specific, practical reasons. Acquisitions frequently bring Azure-based infrastructure and applications into environments primarily running on other platforms. Rather than forcing a complete migration or maintaining separate systems, extending your lakehouse to Azure preserves both the acquired capabilities and your existing architecture.
Regional compliance requirements sometimes mandate that certain data remain in Azure datacenters, particularly for European operations or specific regulated industries. In these cases, you need the ability to query and analyze this Azure-resident data alongside your primary infrastructure without compromising compliance.
Application-specific needs drive Azure adoption as well. Perhaps a critical application runs on Azure and generates data there, or a key partner shares data through Azure services. Your lakehouse needs to accommodate these realities without forcing wholesale platform changes. The goal is seamless extension—your teams continue using their existing tools and workflows while naturally incorporating Azure data when needed.
Requirements for seamless Azure extension
When evaluating whether your lakehouse can reliably extend to Azure, certain capabilities prove essential:
- Platform-agnostic federation: Your solution must treat Azure as just another data source rather than requiring special handling or separate architecture. True federation means querying across your primary infrastructure and Azure within the same workflows, using the same tools.
- Architecture consistency: Whatever patterns you’ve established—whether for security, data organization, or access control—should apply uniformly when you add Azure. Your teams shouldn’t need to learn “the Azure way” of doing things.
- Open table formats: Support for standards like Apache Iceberg ensures that data in Azure remains accessible through the same mechanisms as data anywhere else in your environment.
- Operational simplicity: Your lakehouse should handle Azure connectivity through the same interfaces you already use, require minimal Azure-specific configuration, and allow Azure resources to scale independently without affecting your primary infrastructure.
The goal is for Azure to feel like a natural extension of your existing environment, not a separate system that happens to be connected. Enterprise connectors should support Azure Data Lake Storage alongside your other data sources with consistent behavior across all platforms.
Best practices for adding Azure
When incorporating Azure into your existing lakehouse, start by treating it as an extension rather than a new platform. Store Azure data using the same open table formats you already use elsewhere—Apache Iceberg or similar standards. This consistency means your existing tools, queries, and processes work without modification. There’s no “Azure version” of your data architecture; there’s just your architecture, now accessible in one more location.
Don’t over-rotate on Azure
Avoid the temptation to optimize specifically for Azure or to create Azure-specific patterns. If your teams are accustomed to certain data organization approaches, security models, or access patterns, apply those same patterns in Azure. Consistency across platforms reduces cognitive load and prevents the kind of fragmentation that makes multi-cloud environments difficult to manage.
Separation of compute and storage remains important
Configure Azure storage and compute to operate independently, just as you would in your primary environment. Azure Data Lake Storage (ADLS) provides cost-effective storage, while compute resources should scale based on actual usage. This separation ensures that adding Azure doesn’t lock you into specific capacity or create unexpected cost structures.
Test all integrations
Most importantly, test the integration thoroughly before relying on it for production workloads. Query across your primary infrastructure and Azure, verify that security policies apply correctly, and ensure your teams can access Azure data through their familiar interfaces. The goal is for Azure to become invisible in daily operations—just another place where data happens to live.
Real-world scenarios
Retail acquisition integration
A major retailer operating its data lakehouse on AWS acquired a regional competitor whose infrastructure ran entirely on Azure. Rather than undertaking a costly and time-consuming migration, the retailer extended its existing Starburst-based lakehouse to include Azure. The acquisition’s customer data, inventory systems, and transaction records remained in Azure while analysts seamlessly queried across both platforms. Integration that might have taken a year was accomplished in weeks, and the acquired teams continued using their established Azure applications while gaining access to the parent company’s broader analytics capabilities.
Global manufacturing with regional requirements
A manufacturing company runs its primary data infrastructure on-premises with some workloads in AWS. When European data residency requirements necessitated storing certain operational data in Azure’s European datacenters, the company extended its lakehouse to Azure rather than building separate infrastructure. Engineers query production data across on-premises systems, AWS, and Azure from a single interface. The Azure extension handles compliance requirements without forcing wholesale changes to established analytics practices or requiring separate tools for European operations.
Extending to Azure with Starburst
For organizations already using or evaluating Starburst for their data lakehouse, Azure support provides important flexibility. Starburst treats Azure as just another data source—there’s no separate “Azure version” or different architecture to learn. If you’re running Starburst on AWS, on-premises, or across other platforms, adding Azure connectivity works the same way.
This consistency matters when business needs drive Azure adoption. Whether through acquisition, regional requirements, or application-specific needs, you can incorporate Azure without disrupting established workflows. Your teams continue using the same query interfaces, the same security models, and the same tools they already know.
Starburst on Azure delivers
Starburst delivers a number of benefits for Azure users, including:
- Identical query interface and capabilities across all platforms
- Consistent security policies that extend naturally to Azure
- Support for open table formats like Apache Iceberg across all environments
- No requirement for Azure-specific training or separate operational procedures
- Dynamic scaling that keeps Azure costs aligned with actual usage
Starburst Galaxy, the fully managed offering, handles Azure infrastructure automatically while maintaining consistency with your other platforms. For organizations that need Azure as part of their data landscape, Starburst ensures it integrates reliably without creating operational complexity or requiring separate expertise.
The practical path forward
Azure doesn’t need to be a major decision or a separate strategy. For organizations already operating lakehouses or evaluating lakehouse architectures, Azure can simply be another platform that works when you need it to work.
Choice and flexibility
The key is selecting a lakehouse solution designed for true platform independence from the start. When Azure becomes necessary—whether through acquisition, compliance needs, or application requirements—it should integrate as a natural extension rather than requiring new architecture or creating operational burden.
Starburst and Azure
Starburst’s approach to Azure reflects this philosophy: the same capabilities, the same interfaces, the same operational model. Azure becomes available when you need it, without forcing changes to what already works. For organizations managing data across multiple environments, this reliability matters more than platform-specific optimization.
Azure data lakehouse FAQs
What is a data lakehouse?
A data lakehouse combines the strengths of data warehouses and data lakes. It provides the scalability and cost-effectiveness of a data lake with the performance and governance of a data warehouse. Organizations can store, manage, and analyze all data types in a single platform.
What is a lakehouse?
A lakehouse blends data lake and data warehouse features. It enables unified data storage, access, and analytics by offering transactional guarantees, governance, and security on scalable, cost-effective object storage.
What is Azure data lakehouse?
Azure Data Lakehouse implements lakehouse architecture on Microsoft Azure using services like ADLS as central storage. Organizations can cost-effectively store, govern, and analyze diverse data types while integrating with various analytics and ML tools.
What’s the difference between a data lake and a data lakehouse?
A data lake stores large volumes of structured and unstructured data with low cost and schema flexibility. However, it lacks robust management, governance, and analytics performance. A data lakehouse adds warehouse capabilities such as strong governance, ACID transactions, high query performance, and security to enable enterprise-grade analytics.
What is the difference between Azure and lakehouse?
Azure is Microsoft’s cloud computing platform offering data storage, computing, analytics, and more services. A lakehouse is a specific data architecture that can be implemented on Azure or other clouds. Azure provides the platform. It is one way of organizing and analyzing data using Azure services.
Is Azure Databricks a data lake?
No, Azure Databricks is not a data lake. It’s an analytics platform for big data and machine learning built on Apache Spark. Databricks processes and analyzes data stored in data lakes like ADLS, but provides compute and analytics capabilities rather than storage.
