What is Multi-Cloud Architecture?
Introduction to Multi-Cloud Architecture
In the early 2010s, migrating from on-premises servers to a single cloud provider (like Amazon Web Services) was a major change. However, as enterprise cloud adoption matured, some organizations decided that putting all of their infrastructure (storage, compute, AI models, and security) with a single vendor created too much concentration risk.
If their provider suffered a major regional outage or raised prices, they had little negotiating power and no ready fallback. Different clouds also have different strengths, and a team might prefer one provider’s infrastructure and another’s machine learning tools.
Multi-Cloud Architecture is the strategic deployment of enterprise applications and data infrastructure across two or more distinct public cloud environments (AWS, Azure, GCP) to reduce risk, avoid vendor lock-in, and use the “best of breed” services from each provider.
The Drivers for Multi-Cloud
Enterprises usually adopt multi-cloud architectures for business reasons rather than technical novelty.
1. Avoiding Vendor Lock-In
When an organization builds a data pipeline using proprietary, vendor-specific tools (like AWS Glue writing to Amazon Redshift), migrating to a different cloud provider often requires rewriting much of the pipeline. This lock-in weakens negotiating power. A multi-cloud strategy prioritizes open-source, vendor-neutral technologies (like Apache Spark, Kubernetes, and Apache Iceberg) that run on any of the major clouds.
2. Geographic Compliance and Data Sovereignty
Global enterprises face data protection rules (like GDPR in Europe) and sometimes national or contractual requirements to keep certain data in a specific country. If their primary cloud provider does not have a region in that country, the organization may need a second provider that does.
3. Best-of-Breed Capabilities
A data science team might want to use Google’s Vertex AI and its TPU hardware for model training, while the company’s core operational databases run on Microsoft Azure under an existing enterprise agreement. Multi-cloud architecture lets data move between, or be queried across, these environments.
Multi-Cloud in the Data Lakehouse
Executing a multi-cloud strategy for web applications (using Kubernetes) is relatively straightforward. Doing it for large-scale data analytics is much harder. Data has “gravity”: large datasets are slow and expensive to move, and cloud providers charge egress fees when data leaves their network.
The Open Data Lakehouse is a key enabler for multi-cloud data architecture.
The Role of Open Table Formats
If an organization stores its data in a proprietary format (like Snowflake’s internal storage), that data is locked to Snowflake. If the organization stores its data in Amazon S3 using Apache Iceberg, the data is decoupled from any single compute engine.
- The primary engine (e.g., Dremio running on AWS) queries the Iceberg data.
- If the organization decides to use Google’s BigQuery for a specific workload, BigQuery (through BigLake and BigQuery Omni) can read the same Iceberg tables in S3, or a copy replicated to a Google Cloud bucket. By standardizing on open formats, the data becomes accessible to any compatible compute engine on any cloud.
Federated Query Engines
Moving large volumes of data across clouds incurs significant egress costs. Many multi-cloud architectures use Federated Query Engines (like Trino or Dremio). Instead of copying data from an Azure database to an AWS data lake, the engine (running in AWS) issues a federated SQL query to Azure. The engine uses Compute Pushdown to filter and aggregate the data in Azure, pulling only the reduced result over the network, which lowers egress fees and latency.
Conclusion
Multi-Cloud Architecture is a sign of enterprise IT maturity. It gives the organization more negotiating power with cloud vendors. While it adds real complexity in networking, security (IAM), and governance, standardizing on open-source infrastructure (like Kubernetes) and Open Table Formats (like Apache Iceberg) allows organizations to build resilient, flexible data platforms that span more than one cloud.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.