A deep dive into the leading data virtualization and federated query engines in 2025, comparing performance, governance, pricing, and real-world fit so teams can pick the right platform for unified, fast analytics across fragmented data.
The best data virtualization and federated query engines in 2025 are Starburst Analytics Platform, Denodo Platform, and Google BigQuery Omni. Starburst excels at high-performance cross-lakehouse analytics; Denodo offers robust data governance and cataloging; BigQuery Omni is ideal for multi-cloud federated SQL.
Modern analytics stacks store data in multiple warehouses, lakes, and operational stores. Moving every byte into a single platform adds cost, risk, and latency. Data virtualization and federated query engines solve this by leaving data in place and pushing down SQL through connectors. The result: faster time-to-insight, lower egress fees, and better governance.
We benchmarked engines on standard TPC-DS workloads at 10 TB scale and measured adaptive query planning, caching, and cost-based optimizers.
Support for relational, semi-structured, and streaming sources as well as integration with BI tools, orchestration frameworks, and security providers.
Column-level lineage, role-based access control, data masking, and audit logging were scored because enterprises must meet strict compliance targets in 2025.
We compared pay-as-you-go models, subscription tiers, and BYO compute options to assess overall value.
Setup time, SQL dialect coverage, and self-service cataloging determine adoption speed for mixed engineering and analyst teams.
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Starburst builds on the open-source Trino engine, adding cost-based query optimization, Warp Speed caching, and automated data products. In 2025, Starburst Gravity introduces AI-assisted schema detection that cuts onboarding by 60 percent. The platform delivers sub-second queries on petabyte lakes, integrates with Iceberg and Delta, and provides fine-grained Ranger-compatible security.
Denodo continues to dominate traditional data virtualization with its logical data fabric.
Version 9 adds SmartCache for real-time-to-batch blending and auto-generated APIs for every virtual view. Governance features like lineage graphs and policy-driven masking keep Denodo popular with highly regulated industries.
BigQuery Omni extends BigQuery’s serverless execution to AWS and Azure using Anthos under the hood. In 2025, the service adds Cross-Cloud Materialized Views and usage-based slots. Teams already invested in BigQuery get a unified SQL surface across clouds without data movement.
Dremio Sonar & Arctic
Dremio decouples compute (Sonar) from catalog (Arctic) and ships Reflections for query acceleration. The 2025 release introduces Project Nautilus, enabling automatic Iceberg compaction and multi-table Reflections. Dremio appeals to lakehouse adopters who want open table formats and no vendor lock-in.
Athena’s open pay-per-TB pricing and tight integration with AWS Glue make it the go-to option for serverless analytics inside AWS.
The 2025 update adds native connectors for Snowflake and MongoDB Atlas plus incremental query result reuse that lowers cost up to 35 percent.
IBM’s virtualization service runs on Red Hat OpenShift. The 2025.2 release includes Fabric Designer, letting data stewards build governed views via a no-code canvas. While performance lags the leaders, IBM stands out for deep lineage and NIST-certified encryption.
Azure Synapse Data Virtualization
Azure Synapse links to Cosmos DB, Dataverse, and on-prem SQL Server without ETL. The 2025 GA of Synapse Link for Iceberg boosts analytical pushdown into lakehouse tables. Enterprises locked into Microsoft stacks appreciate single-pane management, but cross-cloud support is limited.
Data Virtuality blends virtualization with ELT automation. The new Hybrid Execution Engine (2025) chooses between pushdown and materialization automatically.
SMBs value the built-in data catalog, yet connector breadth trails Starburst and Denodo.
AtScale focuses on semantic modeling and federated OLAP. The 2025 release introduces AI-generated semantic layers and MDX-over-SQL. It shines for BI acceleration in Tableau or Power BI, but advanced data engineering features are minimal.
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If interactive lakehouse queries are your bottleneck, Starburst or Dremio deliver fastest scans. For enterprise data fabric needs, Denodo or IBM provide cataloging and policy controls.
Multi-cloud shops lean on BigQuery Omni or Starburst. Single-cloud teams often select the native option such as Athena or Synapse.
Serverless models like Athena minimize upfront cost, but heavier governance features in Denodo reduce compliance overhead downstream.
Limit early virtual layers to select privileges while you test lineage and masking rules.
Tools like Starburst Data Products or AtScale semantics abstract complexity for downstream users.
Enable detailed query logs and review skew. Caching or partial materialization often improves slow joins.
Once a federated engine exposes unified SQL access, teams still need a modern workspace to write, version, and share those queries. Galaxy’s lightning-fast SQL editor, context-aware AI copilot, and query governance layer pair perfectly with the platforms above.
Engineers hit the Galaxy desktop app, connect to Starburst or Denodo, and collaborate on trusted, endorsed queries without pasting SQL into Slack.
This synergy lets organizations adopt best-of-breed virtualization engines while maintaining the developer workflow and knowledge management that Galaxy excels at.
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Data virtualization creates a logical layer that lets users query data in multiple physical locations through a single SQL interface, avoiding ETL and enabling real-time access.
A federated query engine focuses on executing distributed SQL across heterogeneous sources with cost-based optimization and pushdown. Most modern platforms combine both concepts.
Google BigQuery Omni and Starburst Analytics Platform lead multi-cloud use cases because they run the same engine across AWS, Azure, and GCP while centralizing governance.
Galaxy provides a developer-first SQL workspace with AI assistance, versioning, and sharing. Connecting Galaxy to a virtualized data layer lets engineers collaborate on trusted queries and serve data products faster.
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