Breaking the Single-Vendor Dependency

In the early days of cloud computing, organizations typically committed to a single cloud provider (AWS, Azure, or Google Cloud). This simplified procurement and architectural design, but it created significant vendor lock-in. If an organization built its entire analytical stack around AWS Redshift, migrating to Google BigQuery or Azure Synapse later would require years of engineering effort to rewrite proprietary SQL and data pipelines.

A multi-cloud architecture intentionally distributes data assets and compute workloads across two or more public cloud providers. An organization might store its primary data lake in Amazon S3, use Google Cloud for its machine learning and Vertex AI capabilities, and run Microsoft Power BI on Azure for enterprise reporting.

This architectural pattern is driven by three primary motivations: avoiding vendor lock-in (maintaining negotiating leverage by ensuring workloads can be moved), leveraging best-of-breed services (using Google for ML, AWS for storage), and risk mitigation (ensuring business continuity if an entire cloud provider experiences an outage).

Challenges of Multi-Cloud Data

Operating a multi-cloud data architecture introduces significant challenges:

Data Gravity and Egress Costs: Data has “gravity” - it is heavy, hard to move, and attracts compute to it. When data stored in AWS S3 is queried by a compute engine in Google Cloud, the data must cross the internet. Cloud providers typically make ingress (bringing data in) free, but charge steep egress fees (sending data out). Moving terabytes of data daily between clouds can result in crippling egress costs.

Network Latency: Cross-cloud queries are inherently slower than intra-region queries. The speed of light and internet routing mean that a query engine in GCP accessing data in an AWS region will experience significantly higher latency than accessing local cloud storage, severely impacting interactive BI performance.

Fragmented Governance: Managing access control across multiple clouds requires maintaining separate IAM (Identity and Access Management) policies. Ensuring that a user has consistent access rights to data regardless of which cloud it resides in is a complex administrative burden.

The Lakehouse as the Multi-Cloud Enabler

The data lakehouse architecture - specifically using Apache Iceberg - is uniquely suited to solving multi-cloud challenges.

Because Iceberg is an open standard, data stored in AWS S3 can be read by Dremio running in Azure, or Spark running in GCP, without any format conversion. The Iceberg REST Catalog provides a vendor-neutral metadata layer that can be accessed from any cloud.

To solve the egress and latency challenges, platforms like Dremio enable an intelligent multi-cloud approach. Dremio clusters can be deployed in both AWS and Azure. A user in Azure querying data located in AWS routes the query through the Dremio control plane; Dremio pushes the execution down to the cluster residing in AWS (bringing the compute to the data), which processes the raw data locally and returns only the final, aggregated result set back across the cloud boundary. This minimizes egress costs (only megabytes of results cross the wire, not terabytes of raw data) and avoids the latency of cross-cloud raw data scans.

This architecture provides the true promise of multi-cloud: a unified logical view of all data across all clouds, with execution intelligently routed to minimize cost and latency.

Learn More

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