BigQuery Omni for Multi-Cloud Analytics

Understanding BigQuery Omni: The Multi-Cloud Analytics Foundation

Most data organizations face a common problem: their data lives everywhere. A financial services company might have customer transaction data in Amazon S3, operational metrics in Azure Data Lake, and product telemetry in Google Cloud Storage. Historically, unifying that data for analytics meant building expensive ETL pipelines, duplicating data across cloud providers, and managing the operational burden of keeping everything in sync.

BigQuery Omni changes that equation. It’s Google Cloud’s answer to the multi-cloud analytics challenge—a service that lets you query data where it lives, without moving it. You run standard SQL against Amazon S3 buckets and Azure Blob Storage directly from BigQuery, using the same BigQuery interface, performance characteristics, and integration patterns you’d use for data in Google Cloud Storage.

For data leaders evaluating managed analytics platforms, this capability matters significantly. When you’re embedding analytics into products, building dashboards for internal stakeholders, or standardizing BI across portfolio companies, the cost and complexity of data movement becomes a real constraint. BigQuery Omni eliminates that constraint.

This article walks through what BigQuery Omni is, how it works architecturally, how it compares to other multi-cloud approaches, and how to integrate it with modern BI platforms like D23’s managed Apache Superset for production-grade self-serve analytics.

What Is BigQuery Omni and Why It Matters

BigQuery Omni is a distributed query execution engine that extends BigQuery’s analytical capabilities beyond Google Cloud infrastructure. Instead of requiring you to ingest or replicate data into BigQuery tables stored in Google Cloud Storage, Omni allows you to define external tables that point directly to data in AWS S3 or Azure Blob Storage, then query those tables using standard SQL.

The key distinction: Omni doesn’t move your data. It moves the compute. When you execute a query against an external table, BigQuery spins up compute resources in the same cloud region where your data lives, executes the query there, and returns results to you. This architecture has several immediate implications:

Cost reduction: You avoid egress charges that come with moving data between clouds. In large-scale analytics, data transfer costs can exceed storage and compute costs combined.

Latency improvement: Queries execute in the region where data resides. For organizations with strict data residency requirements or those optimizing for query performance, this matters significantly.

Operational simplicity: You maintain a single BigQuery interface and control plane across all your data, regardless of where it’s stored. No separate tools, no data duplication, no synchronization headaches.

Governance and compliance: Data stays in the cloud and region where it was created, simplifying compliance with regulations like GDPR, CCPA, and industry-specific data residency rules.

According to Google Cloud’s official announcement, BigQuery Omni was specifically designed for enterprises managing data across multiple cloud providers. It’s not a workaround or a secondary feature—it’s a first-class citizen in the BigQuery ecosystem.

How BigQuery Omni Works: The Architecture

Understanding Omni’s architecture helps explain why it works and where its limitations lie. The system operates in three layers:

The Control Plane (Google Cloud)

Your BigQuery project, datasets, tables, and queries all live in Google Cloud. You connect to BigQuery using standard tools—the web UI, CLI, APIs, or BI platforms—exactly as you normally would. The control plane handles metadata management, query planning, and result aggregation.

The External Data Layer

You define external tables that point to S3 buckets or Azure Blob Storage locations. These tables don’t store data; they’re metadata pointers. When you reference them in a query, BigQuery knows how to locate and read the underlying files.

External tables in BigQuery Omni support multiple file formats:

• Parquet (recommended for analytics—columnar format, excellent compression)
• ORC (similar to Parquet, common in Hadoop ecosystems)
• CSV and JSON (simpler formats, typically less efficient for large-scale queries)
• Avro (row-based format, useful for streaming scenarios)

The Execution Layer (AWS or Azure)

When you query an external table, BigQuery provisions compute resources in the AWS region or Azure region where your data resides. This is where the actual query execution happens. The compute is ephemeral—it spins up for the query, executes, and tears down. You pay for the compute you use, similar to BigQuery’s standard pricing model.

The execution layer communicates back to the control plane with results, which are then returned to your client application or BI tool.

Comparing BigQuery Omni to Alternative Multi-Cloud Approaches

Before implementing Omni, it’s worth understanding how it stacks against other strategies for multi-cloud analytics.

Data Lake Federation (Traditional ETL)

The old approach: build pipelines that extract data from each cloud, transform it, and load it into a central repository (usually in one cloud). This works, but it’s operationally expensive. You’re managing pipelines, handling incremental updates, dealing with failures, and paying for data movement and duplication.

BigQuery Omni eliminates these costs and operational overhead. You query directly without pipelines.

Federated Query Engines (Presto, Trino)

Tools like Presto and Trino also support federated queries across multiple data sources. They’re open-source, flexible, and can query data in various formats and locations.

BigQuery Omni differs in several ways:

• Managed service: You don’t run or manage infrastructure. BigQuery handles provisioning, scaling, and optimization.
• BigQuery integration: Omni is native to BigQuery, so you get all of BigQuery’s features—ML, BI Engine caching, scheduled queries, etc.
• Cloud-native optimization: Execution happens in the cloud where data lives, optimized for that cloud’s storage and networking.
• Cost model: You pay for compute and storage separately; no infrastructure management overhead.

Federated engines are excellent if you need maximum flexibility or are running on-premises. For cloud-native organizations, Omni’s simplicity and integration often wins.

Dual-Cloud Strategies (Separate Analytics in Each Cloud)

Some organizations maintain separate analytics stacks in each cloud—a Looker instance in AWS, a Tableau instance in Azure. This avoids data movement but creates operational complexity: maintaining multiple tools, managing separate datasets, and reconciling metrics across platforms.

BigQuery Omni provides a single, unified analytics platform across clouds, which is operationally superior.

Setting Up BigQuery Omni: Practical Steps

Implementing Omni involves several configuration steps. Here’s a practical walkthrough:

Step 1: Enable BigQuery Omni in Your GCP Project

Start by enabling the BigQuery API and the BigQuery Connection API in your Google Cloud project. You’ll also need to enable the BigQuery Data Transfer Service if you plan to schedule queries.

Step 2: Create Cross-Cloud Connections

BigQuery Omni uses connections to authenticate with AWS or Azure. For AWS, you create an IAM role that BigQuery assumes when executing queries in your S3 buckets. For Azure, you set up a service principal that BigQuery uses to access your Blob Storage accounts.

These connections are stored securely in Google Cloud and never expose credentials directly in queries or configurations.

Step 3: Define External Tables

Once connections are established, you define external tables. Here’s a simplified example for an S3 bucket:

CREATE OR REPLACE EXTERNAL TABLE `project.dataset.customer_transactions`
OPTIONS (
  format = 'PARQUET',
  uris = ['s3://my-data-bucket/transactions/*.parquet'],
  require_partition_filter = false
);

This table definition doesn’t copy data. It tells BigQuery where to find the data and how to interpret it. When you query this table, BigQuery locates the files in S3, provisions compute in the appropriate AWS region, and executes the query.

Step 4: Query and Integrate

Once external tables are defined, you query them like any other BigQuery table:

SELECT 
  customer_id,
  COUNT(*) as transaction_count,
  SUM(amount) as total_spent
FROM `project.dataset.customer_transactions`
WHERE transaction_date >= '2024-01-01'
GROUP BY customer_id;

BigQuery handles the rest. It detects that the table is external, provisions compute in AWS, executes the query there, and returns results.

Performance Considerations and Optimization

BigQuery Omni delivers strong query performance, but there are nuances worth understanding.

Query Latency

For small queries (seconds to minutes), Omni’s latency is comparable to native BigQuery queries. The overhead of provisioning compute in the external cloud is minimal for most analytical queries.

For very large scans or complex joins, you may notice slightly higher latency than querying native BigQuery tables, primarily due to the compute provisioning overhead. This is typically negligible—measured in seconds, not minutes.

Data Format Optimization

Parquet is the recommended format for Omni queries. It’s columnar, compressed, and BigQuery’s query engine is highly optimized for it. If you’re storing data in CSV or JSON, consider converting to Parquet for better performance and lower compute costs.

Partitioning your data by date or region also improves performance. BigQuery can prune partitions before executing queries, reducing the amount of data scanned.

Caching and BI Engine

BigQuery’s BI Engine—an in-memory cache layer—works with Omni tables. Frequently accessed queries can be cached, reducing compute costs and improving dashboard refresh times. For embedded analytics or dashboards, this can be significant.

Materialized Views

For queries that run repeatedly, consider creating materialized views on top of Omni tables. A materialized view pre-computes results and stores them (you choose where—Google Cloud or external). Subsequent queries reference the materialized view, which is faster and cheaper than scanning the raw data repeatedly.

Integrating BigQuery Omni with BI Platforms

BigQuery Omni’s value multiplies when integrated with modern BI platforms. This is where the real analytics work happens—dashboards, reports, self-serve exploration.

D23’s Managed Apache Superset Integration

D23’s managed Apache Superset platform integrates seamlessly with BigQuery Omni. You connect D23 to your BigQuery project, and Superset automatically discovers your datasets—including external tables pointing to S3 or Azure data.

From there, you can:

• Create dashboards that query Omni tables directly
• Enable self-serve analytics where teams explore multi-cloud data
• Leverage D23’s AI-powered text-to-SQL capabilities to let non-technical users write queries naturally
• Embed analytics into products using D23’s API-first architecture

The advantage: you’re not managing another data pipeline or replicating data. Your BI platform queries data where it lives, reducing cost and complexity.

Other BI Platforms

BigQuery Omni works with any BI tool that connects to BigQuery:

• Looker: Native BigQuery integration; Omni tables work seamlessly
• Tableau: Via BigQuery connector; full support for external tables
• Power BI: Via BigQuery connector; Omni tables supported
• Metabase: Open-source BI; BigQuery integration with Omni support
• Mode: Analytics platform with BigQuery connectivity

The integration pattern is the same: connect your BI tool to BigQuery, define external tables, and query them as you would any other table.

Real-World Use Cases for BigQuery Omni

Multi-Cloud Financial Services

A financial services firm uses AWS for trading systems and Azure for compliance and risk management. Customer data is in S3, regulatory data is in Azure Blob Storage. With Omni, they query both datasets in a single SQL query without moving data, enabling real-time compliance dashboards and risk reporting.

Distributed SaaS Analytics

A SaaS company stores customer data in multiple regions—US data in AWS us-east-1, EU data in Azure eu-west-1—for compliance. Using Omni, they maintain a single BigQuery analytics project that queries data in both clouds, providing global analytics while respecting data residency requirements.

Portfolio Company Standardization (Private Equity)

A PE firm acquires companies with analytics in different clouds. Rather than forcing all portfolio companies to migrate to a single cloud, they use BigQuery Omni to create a unified analytics layer on top of existing data. This standardizes KPI reporting and value-creation dashboards across the portfolio without forcing costly data migrations.

Venture Capital Fund Metrics

A VC firm tracks portfolio company metrics, fund performance, and LP reporting across multiple cloud providers. BigQuery Omni lets them maintain a single analytics project that queries data across clouds, enabling real-time fund dashboards and performance tracking.

Cost Analysis: BigQuery Omni vs. Traditional Data Movement

Let’s walk through a concrete cost comparison. Assume you have 10 TB of data in AWS S3 and need to analyze it daily.

Traditional Approach (Copy to BigQuery)

• Data transfer: 10 TB × $0.02/GB = $200 per day
• BigQuery storage: 10 TB × $0.02/GB-month = ~$6.50/day
• BigQuery compute: 10 TB scan × $6.25/TB = $62.50 per query (assuming daily query)
• Total daily cost: ~$269
• Annual cost: ~$98,000

BigQuery Omni Approach

• Data transfer: $0 (data stays in S3)
• BigQuery storage: $0 (metadata only)
• BigQuery compute: 10 TB scan × $6.25/TB = $62.50 per query (Omni compute priced similarly to BigQuery compute)
• Total daily cost: ~$63
• Annual cost: ~$23,000

For a 10 TB dataset queried daily, Omni saves approximately $75,000 annually compared to copying data. For larger datasets or more frequent queries, savings are even more dramatic.

Security and Compliance with BigQuery Omni

Data Residency

One of Omni’s strongest advantages is data residency compliance. Data never leaves the cloud where it’s stored. For organizations subject to GDPR, HIPAA, or other data residency regulations, this is critical.

Authentication and Authorization

BigQuery Omni uses cloud-native authentication:

• AWS: IAM roles and policies control which BigQuery service accounts can access which S3 buckets
• Azure: Service principals and role-based access control (RBAC) manage permissions

These integrations are transparent to end users. When a user runs a query in Superset or another BI tool, BigQuery authenticates using the configured connection, and the query executes with the appropriate cloud permissions.

Encryption

Data in transit between BigQuery and external storage is encrypted. Data at rest is encrypted by the respective cloud provider (AWS KMS for S3, Azure Key Vault for Blob Storage).

Audit Logging

All queries executed against Omni tables are logged in BigQuery’s audit logs. You can track who queried what, when, and from where. This audit trail is essential for compliance and security investigations.

Limitations and Considerations

BigQuery Omni is powerful, but it’s not a universal solution. Understanding its limitations helps you decide if it’s right for your use case.

Supported Cloud Providers

Currently, BigQuery Omni supports AWS S3 and Azure Blob Storage. Google Cloud Storage is not supported (you’d use native BigQuery tables instead). If you have data in other clouds or on-premises systems, you’d need a different approach.

Query Complexity

While Omni supports standard SQL, some advanced BigQuery features have limitations when querying external tables:

• Machine Learning (BigQuery ML): Limited support; some models may not work with external tables
• Certain functions: Some BigQuery-specific functions may not be optimized for external tables
• Nested data: Complex nested structures in JSON or Avro may have performance implications

For most analytical queries, these limitations don’t matter. But for advanced use cases, you may need to materialize data into native BigQuery tables.

Metadata Refresh

When data in S3 or Azure changes, BigQuery’s external table metadata doesn’t automatically refresh. You need to manually refresh the table metadata or set up scheduled refresh jobs. This is typically not a problem for analytical workloads (which are usually batch-oriented), but it matters for real-time scenarios.

Cost Predictability

BigQuery Omni’s compute costs are predictable (per TB scanned), but they can be harder to forecast than native BigQuery queries if you’re not careful about query optimization. Unoptimized queries that scan large amounts of data can become expensive quickly.

Best Practices for BigQuery Omni Implementation

1. Optimize Your Data Format

Convert raw data to Parquet format before querying with Omni. Parquet’s columnar structure and compression reduce scan volume and improve query performance.

2. Implement Partitioning

Partition your data by date, region, or other logical divisions. This allows BigQuery to prune partitions and scan only relevant data.

3. Use Materialized Views Strategically

For frequently accessed datasets or complex transformations, create materialized views. This pre-computes results and reduces repeated query costs.

4. Monitor Query Costs

Set up BigQuery cost monitoring and alerts. Omni’s cost model is transparent, but unoptimized queries can become expensive. Regular monitoring helps you catch and fix inefficient queries.

5. Leverage BI Engine Caching

Enable BI Engine caching for dashboards and frequently accessed queries. The in-memory cache significantly improves performance for dashboard refreshes.

6. Document Your External Table Schema

Maintain clear documentation of your external tables, including the S3 or Azure paths they point to, the data format, partitioning scheme, and refresh frequency. This helps your team understand the data landscape and troubleshoot issues.

7. Test Query Performance

Before deploying dashboards or reports, test query performance against your Omni tables. Understand the scan volume and execution time, and optimize as needed.

Integrating Omni with D23 for Production Analytics

When you combine BigQuery Omni’s multi-cloud querying with D23’s managed Apache Superset platform, you get a production-grade analytics stack without the operational overhead.

Here’s how this integration works in practice:

Setup

1. Configure BigQuery Omni external tables pointing to your S3 and Azure data
2. Connect D23 to your BigQuery project
3. D23 automatically discovers your external tables
4. Create dashboards and charts using these tables

Self-Serve Analytics

D23’s self-serve BI capabilities let business teams explore multi-cloud data without writing SQL. They click through dimensions, apply filters, and drill down into data—all querying your Omni tables directly.

AI-Powered Analytics

D23’s text-to-SQL capabilities (powered by LLMs and AI) let non-technical users ask questions in natural language. The AI translates these questions into SQL queries against your Omni tables, enabling truly self-serve analytics.

Embedded Analytics

If you’re embedding analytics into a product, D23’s API-first architecture makes it straightforward. Your product can query Omni tables through D23’s APIs, returning visualizations that you embed directly in your application.

Data Consulting

D23 includes expert data consulting services. Their team can help you optimize your Omni setup, design efficient external table schemas, and architect analytics solutions that scale.

This combination—BigQuery Omni for multi-cloud data access, D23 for managed analytics—eliminates the need to manage your own BigQuery infrastructure, hire specialized BigQuery engineers, or build custom BI tools.

Advanced: Text-to-SQL and MCP for Omni

For organizations using D23’s MCP (Model Context Protocol) server for analytics, BigQuery Omni opens additional possibilities.

MCP servers allow AI models to interact with your analytics platform programmatically. Combined with BigQuery Omni, this enables:

• Autonomous analytics: AI agents that automatically generate insights from multi-cloud data
• Natural language queries: Users ask questions conversationally, and the AI generates optimized SQL against Omni tables
• Predictive dashboards: AI systems that proactively surface anomalies or opportunities in your multi-cloud data

This is where analytics becomes truly self-serve and intelligent.

Comparing Omni to Preset and Other Superset Hosting

Preset is Astronomer’s managed Superset offering. It’s a solid platform, but it doesn’t solve the multi-cloud data problem. Preset still requires you to either move data into Superset’s data warehouse or maintain external connections to each data source.

BigQuery Omni + D23 differs:

• Unified multi-cloud query layer: BigQuery Omni provides a single SQL interface to data across clouds
• No data movement: Data stays where it is; you query it in place
• Cost efficiency: You avoid egress charges and data duplication
• Compliance: Data residency is maintained naturally

For organizations with data in multiple clouds, this is a significant advantage over traditional Superset hosting.

The Future of Multi-Cloud Analytics

BigQuery Omni represents a shift in how enterprises approach analytics. Rather than centralizing data (which is expensive and complex), you’re centralizing the analytics interface while keeping data distributed.

This pattern will likely expand. We’ll see more analytics platforms (both managed and open-source) adopting similar approaches. The industry is moving toward federated, distributed analytics rather than centralized data warehouses.

For your organization, this means the time to adopt BigQuery Omni is now. As the approach becomes more standard, the tools and practices around it will mature, and the cost advantages will only increase.

Conclusion: Multi-Cloud Analytics Without the Complexity

BigQuery Omni solves a real problem for modern data organizations: how to analyze data distributed across multiple clouds without the cost, complexity, and compliance headaches of data movement.

By querying data where it lives—in S3, Azure Blob Storage, or Google Cloud Storage—you reduce costs, simplify operations, and maintain compliance with data residency requirements.

When integrated with a modern BI platform like D23’s managed Apache Superset, you get a complete analytics solution: multi-cloud data access, self-serve BI for business teams, AI-powered analytics for intelligent insights, and expert consulting to optimize your setup.

If your organization manages data across multiple clouds, BigQuery Omni deserves serious consideration. The financial and operational benefits are substantial, and the implementation is straightforward. Start with a pilot—define a few external tables, run some queries, and measure the cost savings. You’ll likely find that BigQuery Omni pays for itself within months.

For more information on BigQuery Omni’s capabilities, explore Google Cloud’s official documentation and technical resources. To see how BigQuery Omni integrates with modern BI platforms, visit D23 and explore how managed Apache Superset can simplify your analytics operations across clouds.