BigQuery Omni: Querying Multi-Cloud Data Without Moving It

For years, the "Data Gravity" problem has dictated cloud strategy. The sheer cost of data egress and the latency involved in moving petabytes of information often forced organizations to centralize their entire analytics stack within a single cloud provider. Google Cloud Platform (GCP) challenged this paradigm by introducing BigQuery Omni, a multi-cloud analytics solution that allows users to analyze data stored in Amazon S3 or Azure Blob Storage without moving or copying it to GCP.

BigQuery Omni represents a fundamental shift in how we perceive data residency and processing. Instead of the traditional Extract-Transform-Load (ETL) pipelines that shuttle data across the internet, Google has effectively decoupled the BigQuery query engine (Dremel) from the storage layer. By running the compute engine locally within the target cloud environment (AWS or Azure), Google enables a "compute-to-data" model. This approach minimizes egress costs, enhances security by keeping data within its original perimeter, and provides a unified interface for data engineers who no longer need to learn cloud-specific tooling for every silo.

From a senior architect's perspective, the genius of BigQuery Omni lies in its leverage of Anthos. By containerizing the Dremel engine and deploying it via Anthos clusters on rival clouds, Google has created a truly distributed database architecture. Users interact with the familiar BigQuery UI or API in GCP, while the heavy lifting of scanning and filtering happens on the remote cloud's infrastructure. This provides a consistent "single pane of glass" experience across a fragmented multi-cloud estate.

The Architecture of Multi-Cloud Querying

The architecture of BigQuery Omni is built on a clear separation between the Control Plane and the Data Plane. The Control Plane resides in GCP, managing the user interface, metadata, and query optimization. The Data Plane, powered by Anthos, resides in the remote cloud (e.g., AWS US-East-1). When a query is issued, the Control Plane sends the execution plan to the remote Data Plane, which processes the data locally and returns only the final result set.

Implementing BigQuery Omni with Python

To implement BigQuery Omni, you must first establish a connection resource that defines the identity used to access the remote storage. In AWS, this involves creating an IAM OIDC provider that trusts Google's identity. Once the connection is established, you can query external tables using the standard BigQuery client libraries.

The following Python example demonstrates how to create an external table definition pointing to an S3 bucket and execute a query against it.

from google.cloud import bigquery

# Initialize the BigQuery client
client = bigquery.Client(project='my-multi-cloud-project')

# Configuration for the external AWS S3 table
# Ensure the 'aws-us-east-1' connection has already been created in GCP
connection_id = "my-multi-cloud-project.aws-us-east-1.s3-connection"
table_id = "my-multi-cloud-project.external_aws_dataset.inventory_data"

external_config = bigquery.ExternalConfig("PARQUET")
external_config.source_uris = ["s3://my-production-bucket/inventory/*.parquet"]
external_config.connection_id = connection_id

table = bigquery.Table(table_id)
table.external_data_configuration = external_config

# Create the external table reference in the BigQuery catalog
table = client.create_table(table, exists_ok=True)

# Querying the data across clouds
query = f"""
    SELECT 
        product_id, 
        SUM(quantity) as total_stock
    FROM `{table_id}`
    WHERE region = 'east'
    GROUP BY 1
    ORDER BY total_stock DESC
    LIMIT 10
"""

query_job = client.query(query)
results = query_job.result()

for row in results:
    print(f"Product: {row.product_id}, Stock: {row.total_stock}")

Service Comparison: Multi-Cloud Analytics

Data Flow and Request Lifecycle

Understanding the request lifecycle is crucial for debugging performance. When a user submits a SQL statement, the BigQuery Control Plane determines if the data resides in an external cloud. If so, it dispatches the query to the local BigQuery slots running in that specific region. The data is read from the local object store (S3/Blob), processed, and only the summarized results are sent back across the network to GCP.

Best Practices for BigQuery Omni

To maximize the efficiency of multi-cloud queries, architects should focus on file formats and partitioning. Since the compute engine is scanning data over a local network within the remote cloud, using columnar formats like Parquet or Avro significantly reduces the I/O required. Furthermore, implementing Hive-compatible partitioning on S3 or Azure Blob Storage allows BigQuery Omni to perform "partition pruning," skipping irrelevant data folders entirely.

Security should be handled via Workload Identity Federation. Avoid using long-lived access keys. By using OIDC, the BigQuery Omni service account can assume a temporary IAM role in AWS or Azure, ensuring a short-lived, least-privileged security posture.

Conclusion

BigQuery Omni is more than just a feature; it is a strategic tool for the modern enterprise. By breaking down the barriers between cloud providers, it allows organizations to adopt a "best-of-breed" strategy without the technical debt of complex ETL pipelines. For the senior architect, it provides a path to unified governance and analytics, where the physical location of the data becomes an implementation detail rather than a roadblock. As we move toward a more decentralized data landscape, the ability to query data where it lives—securely and efficiently—will be the hallmark of a mature cloud architecture.

https://cloud.google.com/bigquery/docs/omni-introduction https://cloud.google.com/anthos/docs/concepts/overview https://cloud.google.com/bigquery/docs/omni-aws-create-connection