BigQuery Omni Cross Cloud Analytics Revolution

• Run BigQuery SQL against data in AWS S3 and Azure Blob Storage without migrating data.
• Perform near-real-time cross-cloud joins between datasets that live in different clouds, using the same BigQuery dialect and tooling.
• Reduce egress, improve latency, and avoid lift-and-shift migrations.

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What powers BigQuery Omni?

• Anthos-based runtime: Google uses Anthos to deploy a managed BigQuery compute runtime into the AWS or Azure region that hosts your data. This runtime is managed by Google — you don’t provision it.
• Local compute near data: The runtime executes inside the same cloud region as the storage (S3 or Blob), minimizing cross-cloud egress and latency.
• Unified operations and governance: Access control, auditing, and operational policies are enforced from your Google Cloud control plane so governance can remain centralized.
• Runtime optimizations: Query planning pushes processing close to the data sources and minimizes unnecessary data movement.

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Design-time flow (planning and integration)

1. Discover: Inventory datasets and their cloud locations (AWS S3, Azure Blob, or GCP).
2. Model & optimize: Design table models and queries to reduce cross-cloud data exchange. Decide whether in-place processing is sufficient or if selective migration is needed.
3. Integrate: Connect those queries into existing orchestration and pipelines, re-using your BigQuery SQL and tools.

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Runtime flow (what happens when you run a query)

1. Submit: You run a SQL query via the BigQuery UI, CLI, or API.
2. Route: BigQuery’s control plane analyzes the query and routes parts that reference external cloud storage to the Anthos-hosted runtime in that cloud.
3. Execute: The runtime processes data in-place against S3 or Blob Storage.
4. Return & combine: Only results or minimized intermediate data are returned to BigQuery’s control plane, which combines results and delivers the final output.

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How to query external cloud data (example)

• Create an external table referencing external storage (example syntax — adapt to your environment and the connection resource created during setup):

CREATE EXTERNAL TABLE `my_dataset.sales_external`
OPTIONS (
  format = 'PARQUET',
  uris = ['s3://my-bucket/path/to/*.parquet'],
  connection = 'projects/PROJECT/locations/LOCATION/connections/CONNECTION_ID'
);

• Query and join with a native BigQuery table:

SELECT a.order_id, a.amount, b.customer_name
FROM `my_dataset.sales_external` AS a
JOIN `my_dataset.customers` AS b
  ON a.customer_id = b.customer_id
LIMIT 100;

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Why organizations adopt BigQuery Omni

• Low latency: Processing occurs near the data, reducing round-trip delays.
• Lower egress costs: Avoid large-scale data transfer out of cloud providers by executing close to the data.
• Near-real-time cross-cloud analytics: Join and analyze across clouds without duplicating pipelines.
• Simplified operations: Reduce the need for complex ETL and synchronization workflows.
• Centralized security and governance: Enforce consistent policies and auditing from Google Cloud.

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Primary use cases

• Multi-cloud enterprises that must analyze datasets spread across providers without consolidation.
• Legacy or regulated datasets that are costly or risky to migrate.
• Teams needing combined, near-real-time analytics across cloud storage systems.
• Situations where building and maintaining cross-cloud ETL pipelines is impractical or slow.

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Quick comparison: BigQuery Omni vs traditional data movement

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Exam tip / practical guidance

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Summary

• BigQuery Omni documentation
• Anthos overview
• BigQuery external data sources and connections
• AWS S3
• Azure Blob Storage