bundle-course—sap-scm-supply-chain-management By Uplatz<\/a><\/h3>\nArchitecturally, Omni is an elegant extension of BigQuery’s foundational design principle: the separation of compute and storage. This is made possible by Anthos, Google’s Kubernetes-based multi-cloud application platform, which allows Google to deploy and manage its Dremel query engine on infrastructure within AWS and Azure regions.<\/span>4<\/span> This approach preserves the serverless, fully managed user experience while co-locating compute resources with the data, yielding significant benefits in performance and cost efficiency.<\/span>3<\/span> The security model is robust, federating with native cloud identity providers like AWS IAM and Azure Active Directory, thereby allowing enterprises to maintain control over their data while granting delegated access.<\/span>3<\/span><\/p>\nIn the competitive landscape, BigQuery Omni distinguishes itself as a fully managed, serverless offering. It presents a more integrated and less management-intensive alternative to powerful open-source-based federated engines like Starburst (Trino) and offers a fundamentally different, federation-first approach compared to the ingestion-centric models of data lakehouse platforms like Databricks.<\/span>9<\/span><\/p>\nThis report provides a comprehensive analysis of BigQuery Omni’s architecture, capabilities, and strategic implications. It concludes that for organizations already invested in the Google Cloud ecosystem but contending with significant data gravity in AWS or Azure, BigQuery Omni represents a transformative strategic asset. It effectively dissolves data silos, reduces total cost of ownership for multi-cloud analytics, and positions the enterprise data warehouse not as a centralized repository, but as a distributed, intelligent “brain” capable of seeing and analyzing data, wherever it may live.<\/span><\/p>\n <\/p>\n
The Architectural Blueprint of a Multi-Cloud Engine<\/b><\/h2>\n
<\/p>\n
The architecture of BigQuery Omni is not an incidental feature but a deliberate extension of the core design principles that have defined BigQuery since its inception. Its ability to operate across cloud boundaries is a direct result of a series of strategic technology choices, from its foundational compute-storage separation to its use of a modern, container-based multi-cloud substrate.<\/span><\/p>\n <\/p>\n
The Foundational Principle: Decoupling Compute and Storage<\/b><\/h3>\n
<\/p>\n
The capacity for BigQuery Omni to function as a multi-cloud engine is a direct consequence of a foundational architectural decision made at BigQuery’s inception: the decoupling of compute and storage resources.<\/span>4<\/span> Unlike traditional monolithic data warehouses where compute nodes and storage are tightly integrated, BigQuery was designed with a clear separation between its Dremel query engine (compute) and its Colossus distributed file system (storage).<\/span>3<\/span><\/p>\nThis separation was initially intended to allow for independent, on-demand, and elastic scaling of both resources within Google Cloud’s infrastructure.<\/span>5<\/span> An organization could scale its storage to petabytes without needing to pre-provision a corresponding level of expensive compute, and conversely, it could burst its compute capacity to handle complex analytical workloads without altering its storage footprint. This design created a stateless and inherently portable compute layer. It is this portability that now enables Google to deploy the Dremel engine outside its own data centers and directly into the environments of other cloud providers\u2014a feat that would require a fundamental and disruptive redesign for platforms with tightly coupled architectures.<\/span><\/p>\n <\/p>\n
The Role of Anthos: The Kubernetes-Powered Multi-Cloud Substrate<\/b><\/h3>\n
<\/p>\n
While the decoupled architecture makes Omni theoretically possible, Google Anthos is the critical enabling technology that makes it physically and operationally viable.<\/span>4<\/span> Anthos is Google’s managed application platform, built upon the open-source Kubernetes container orchestration system, designed to provide a consistent environment for building, deploying, and managing applications across on-premise data centers and multiple public clouds.<\/span>14<\/span><\/p>\nIn the context of BigQuery Omni, Google uses Anthos to package the Dremel query engine into containers and deploy it onto clusters within designated AWS and Azure regions.<\/span>4<\/span> Crucially, these Anthos clusters are fully managed by Google Cloud. This abstracts the entire underlying infrastructure\u2014virtual machines, networking, Kubernetes management\u2014away from the end-user.<\/span>3<\/span> This ensures that even when operating on third-party infrastructure, BigQuery Omni retains the hallmark serverless experience of its native GCP counterpart. The user does not need to provision, configure, or manage any clusters; they simply submit a query, and the system handles the rest.<\/span>4<\/span><\/p>\n <\/p>\n
Dissecting the Control and Data Planes: How Queries Traverse Cloud Boundaries<\/b><\/h3>\n
<\/p>\n
BigQuery Omni’s architecture is best understood as a distributed system with two distinct components: a centralized control plane and one or more remote data planes.<\/span>3<\/span><\/p>\n\n- The Control Plane:<\/b> This component resides entirely within Google Cloud. It serves as the central “brain” and single point of management for the entire system. When a user submits a query through the BigQuery UI, the bq command-line tool, or an API, it is the control plane that receives it. This plane is responsible for all initial query processing steps: authentication, authorization, SQL parsing, query optimization, and metadata management. It leverages BigQuery’s distributed metadata system, CMETA, to understand the schemas and locations of external tables.<\/span>3<\/span><\/li>\n
- The Data Plane:<\/b> This component is what makes Omni unique. It consists of the Google-managed Anthos clusters running the containerized Dremel engine within a customer’s chosen AWS or Azure region.<\/span>3<\/span> The data plane is deployed in the same region as the target data (e.g., in aws-us-east-1 to query data in an S3 bucket in that region). This is where the actual data scanning, filtering, aggregation, and computation\u2014the “heavy lifting” of the query\u2014occurs.<\/span><\/li>\n<\/ul>\n
The communication between the control plane in GCP and the data plane in AWS or Azure is facilitated by a secure, Google-managed VPN connection, ensuring that query instructions and results are transmitted privately.<\/span>3<\/span><\/p>\n <\/p>\n
Data Flow Analysis: A Step-by-Step Walk-through<\/b><\/h3>\n
<\/p>\n
The interaction between the control and data planes manifests in two primary data flow patterns, depending on the nature of the query.<\/span><\/p>\n\n- Query Flow for SELECT Statements:<\/b> When a user executes a standard SELECT query to analyze data and view results in the console, the process is as follows:<\/span><\/li>\n<\/ul>\n
\n- Submission:<\/b> The user submits a GoogleSQL query to the BigQuery control plane in GCP.<\/span><\/li>\n
- Processing & Forwarding:<\/b> The control plane parses and optimizes the query, then sends the execution plan over the VPN to the appropriate data plane in the target AWS or Azure region.<\/span><\/li>\n
- Execution:<\/b> The Dremel engine in the data plane reads the data directly from the specified Amazon S3 bucket or Azure Blob Storage container. It performs all necessary computations locally within that region.<\/span><\/li>\n
- Result Return:<\/b> The final result set is transmitted back across the VPN to the BigQuery control plane.<\/span><\/li>\n
- Display:<\/b> The control plane presents the results to the user in the Google Cloud console. It is important to note that there are limitations on the size of the result set that can be returned this way, currently 10 GB for interactive queries.<\/span>3<\/span><\/li>\n<\/ol>\n
\n- Export Flow for EXPORT DATA Statements:<\/b> For queries that generate very large result sets, a more efficient pattern is used:<\/span><\/li>\n<\/ul>\n
\n- Submission:<\/b> The user submits an EXPORT DATA query, which includes a destination path within an S3 bucket or Blob Storage container in the source cloud.<\/span><\/li>\n
- Processing & Forwarding:<\/b> The control plane processes the query and forwards the execution plan to the data plane, just as in the SELECT flow.<\/span><\/li>\n
- Execution:<\/b> The Dremel engine in the data plane reads the source data and performs the computations locally.<\/span><\/li>\n
- Direct Write-Back:<\/b> Instead of returning the results to GCP, the data plane writes the final result set <\/span>directly<\/span><\/i> to the specified destination path in the S3 bucket or Blob Storage container within the same cloud and region. In this scenario, no large result data traverses the cross-cloud boundary, completely eliminating egress costs for the query output.<\/span>3<\/span><\/li>\n<\/ol>\n
This dual-flow model provides flexibility, allowing for interactive analysis of smaller result sets while offering a highly cost-effective method for large-scale data transformation and processing tasks.<\/span><\/p>\n\n\n\n| Step<\/b><\/td>\n | Action<\/b><\/td>\n | Location<\/b><\/td>\n | Components Involved<\/b><\/td>\n<\/tr>\n\n| 1. Query Submission<\/b><\/td>\n | User writes and executes a GoogleSQL query.<\/span><\/td>\n | Google Cloud<\/span><\/td>\n | User, BigQuery UI\/API\/CLI<\/span><\/td>\n<\/tr>\n\n| 2. Control Plane Processing<\/b><\/td>\n | Query is received, authenticated, parsed, and optimized. Execution plan is generated.<\/span><\/td>\n | Google Cloud<\/span><\/td>\n | BigQuery Control Plane, CMETA<\/span><\/td>\n<\/tr>\n\n| 3. Job Transmission<\/b><\/td>\n | Optimized execution plan is sent to the remote data plane.<\/span><\/td>\n | Secure VPN<\/span><\/td>\n | BigQuery Control Plane -> BigQuery Data Plane<\/span><\/td>\n<\/tr>\n\n| 4. Data Plane Execution<\/b><\/td>\n | Dremel engine reads data from the external source and performs all computations.<\/span><\/td>\n | AWS \/ Azure Region<\/span><\/td>\n | BigQuery Data Plane (Anthos\/Dremel), Amazon S3 \/ Azure Blob Storage<\/span><\/td>\n<\/tr>\n\n| 5a. Result Return (SELECT)<\/b><\/td>\n | The final, aggregated result set is sent back to the control plane.<\/span><\/td>\n | Secure VPN<\/span><\/td>\n | BigQuery Data Plane -> BigQuery Control Plane<\/span><\/td>\n<\/tr>\n\n| 5b. Result Export (EXPORT)<\/b><\/td>\n | The final result set is written directly to a specified path in the source cloud’s storage.<\/span><\/td>\n | AWS \/ Azure Region<\/span><\/td>\n | BigQuery Data Plane -> Amazon S3 \/ Azure Blob Storage<\/span><\/td>\n<\/tr>\n\n| 6. Result Display (SELECT)<\/b><\/td>\n | The control plane receives the result set and displays it to the user.<\/span><\/td>\n | Google Cloud<\/span><\/td>\n | BigQuery Control Plane, User<\/span><\/td>\n<\/tr>\n\n| Table 1: Architectural Data Flow for a Cross-Cloud Query<\/b><\/td>\n | <\/td>\n | <\/td>\n | <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n <\/p>\n The BigLake Synergy: A Unified Metadata and Governance Layer<\/b><\/h3>\n <\/p>\n While BigQuery Omni provides the federated <\/span>compute<\/span><\/i> capability, Google’s BigLake technology provides the corresponding federated <\/span>storage and governance<\/span><\/i> layer, and the two are designed to work in synergy.<\/span>17<\/span> BigLake is a storage engine that allows users to create unified, BigQuery-managed tables over data stored in various formats and locations, including Google Cloud Storage, AWS S3, and Azure Blob Storage.<\/span>17<\/span><\/p>\nWhen used with Omni, BigLake acts as a crucial metadata abstraction layer. An analyst can define a BigLake table that points to a set of Parquet files in an S3 bucket. From the user’s perspective, this “BigLake table” appears and behaves just like a native BigQuery table. The true power of this integration is that it allows BigQuery’s robust, fine-grained security controls\u2014such as row-level security, column-level security, and data masking\u2014to be defined on the BigLake table within GCP and then be consistently enforced by the Omni query engine when it accesses the underlying data in AWS or Azure.<\/span>5<\/span><\/p>\nThis relationship is not merely convenient; it is fundamental to making Omni an enterprise-ready solution. Without BigLake, governance would be fragmented and difficult to manage. With BigLake, Omni becomes part of a cohesive multi-cloud data lakehouse strategy where a single set of governance policies can be applied to data, regardless of its physical location.<\/span><\/p>\n <\/p>\n BigQuery Omni as a Federated Analytics Brain<\/b><\/h2>\n <\/p>\n At its core, BigQuery Omni embodies the principles of a federated query engine, acting as an intelligent control plane or “brain” that can orchestrate analytics across a distributed data landscape. This approach represents a paradigm shift from the traditional data warehousing model, which requires centralizing all data before analysis can occur.<\/span><\/p>\n <\/p>\n The “Query in Place” Paradigm<\/b><\/h3>\n <\/p>\n The central tenet of BigQuery Omni is the ability to analyze data where it resides.<\/span>3<\/span> This “query in place” model delivers two primary strategic advantages: cost reduction and performance acceleration.<\/span><\/p>\nThe most significant financial barrier to multi-cloud analytics has historically been data egress fees\u2014the charges levied by cloud providers for moving data out of their network.<\/span>1<\/span> For organizations with petabytes of data, these costs can be prohibitive, effectively locking data into the cloud where it was generated.<\/span>4<\/span> By deploying its compute engine to the data’s location, Omni sidesteps this issue entirely for the raw data processing. The only data that needs to traverse the cloud boundary is the relatively small result set of an aggregated query, or in the case of an EXPORT DATA query, no result data moves at all.<\/span>3<\/span><\/p>\nFrom a performance perspective, this paradigm eliminates the immense network latency associated with transferring massive datasets across the public internet.<\/span>3<\/span> Instead of a multi-hour or multi-day ETL process to copy data into GCP before a single query can be run, analysts can get insights in minutes by running queries directly against the source data.<\/span><\/p>\n <\/p>\n Cross-Cloud Joins: Capabilities and Limitations<\/b><\/h3>\n <\/p>\n One of the most powerful manifestations of Omni’s federated capability is the cross-cloud join. This feature allows a single GoogleSQL query to join data from a table native to BigQuery in GCP with a BigLake table that references data in AWS S3 or Azure Blob Storage.<\/span>7<\/span> For example, an analyst could join a customers table in BigQuery with terabytes of transaction_logs stored in S3 to enrich customer profiles with their latest activity, all within a single, expressive SQL statement.<\/span><\/p>\nThe execution of such a query is a sophisticated orchestration. The BigQuery optimizer determines the most efficient plan, which typically involves pushing down as much computation as possible to the respective data planes. A subquery might be sent to the Omni data plane in AWS to filter and aggregate the transaction logs, with only the necessary intermediate result being transferred back to GCP to be joined with the native BigQuery table.<\/span><\/p>\nHowever, this powerful capability is subject to important limitations. The amount of data that can be transferred from a remote region as part of a subquery is capped at 60 GB.<\/span>3<\/span> This means that queries must be structured to perform significant filtering and aggregation on the remote side to ensure the intermediate data transferred back to GCP for the join remains under this threshold. Additionally, certain complex query operators and functions may be restricted in cross-cloud join scenarios.<\/span>5<\/span> Early iterations of the product had more severe limitations, such as the inability to perform any cross-location join in a single query, highlighting the platform’s rapid evolution and Google’s continued investment in this area.<\/span>5<\/span><\/p>\n <\/p>\n Federated Queries vs. Data Virtualization<\/b><\/h3>\n <\/p>\n BigQuery Omni firmly positions itself as a federated query engine, a class of system designed to provide a unified SQL interface over multiple, heterogeneous data sources without requiring data consolidation.<\/span>9<\/span> It is useful to distinguish this from traditional data virtualization.<\/span><\/p>\nWhile both concepts aim to abstract data location, their implementation differs significantly. Many data virtualization tools rely on connectors that translate a central query into the native dialect of a source system and “push down” certain operations (like filters), but often end up pulling large amounts of raw or semi-processed data back to a central engine for complex joins and aggregations.<\/span><\/p>\nBigQuery Omni’s architecture is more robust. It does not simply use a lightweight connector; it deploys a full-fledged, massively parallel processing (MPP) query engine (Dremel) into the remote environment.<\/span>4<\/span> This allows for highly complex and computationally intensive operations to be performed entirely on the remote side, minimizing data movement and leveraging an engine renowned for its performance at petabyte scale.<\/span><\/p>\n <\/p>\n The On-Premise Question: Deconstructing the Promise and Reality<\/b><\/h3>\n <\/p>\n A critical part of the “federated brain” concept is its reach. The user query explicitly asks about on-premise data, and some marketing materials and early announcements for Omni have indeed suggested this capability, often in the context of Anthos’s hybrid-cloud nature.<\/span>14<\/span> The underlying technology, Anthos, is designed to run consistently in on-premise data centers, which logically implies that the Dremel engine could, in theory, be deployed there as well.<\/span><\/p>\nHowever, a rigorous examination of the current technical documentation and implementation guides reveals a significant gap between this architectural potential and the product’s present-day reality. All official documentation, tutorials, and API specifications for BigQuery Omni exclusively list Amazon S3 and Azure Blob Storage as supported external data sources.<\/span>3<\/span> The separate “federated queries” feature within BigQuery, which uses the EXTERNAL_QUERY function, is designed for connecting to other <\/span>Google Cloud databases<\/span><\/i> like Cloud SQL and Spanner, not for reaching into on-premise systems like Oracle or SQL Server.<\/span>21<\/span><\/p>\nTherefore, as of this analysis, BigQuery Omni does <\/span>not<\/span><\/i> function as a federated analytics brain for on-premise data. While this remains a plausible future direction for the product given its Anthos foundation, any organization considering adoption must understand that its current scope is strictly limited to the major public cloud object stores. This is a crucial clarification that tempers the “sees everything” marketing promise with the concrete realities of the current implementation.<\/span><\/p>\n <\/p>\n A Unified Security and Governance Framework<\/b><\/h2>\n <\/p>\n For any multi-cloud solution to be viable in an enterprise context, it must offer a security and governance model that is not only robust but also consistent and manageable across disparate environments. BigQuery Omni addresses this by building its security framework on the principle of federated identity and delegated trust, integrating with the native identity and access management (IAM) systems of AWS and Azure.<\/span><\/p>\n <\/p>\n Integrating with Native Cloud Identities: AWS IAM and Azure AD<\/b><\/h3>\n <\/p>\n BigQuery Omni’s security model cleverly avoids the pitfalls of managing a separate set of credentials. Instead, it leverages a secure federation pattern to assume identities within the target cloud environments, ensuring that the customer retains ultimate control over data access.<\/span>3<\/span><\/p>\n\n- Integration with AWS IAM:<\/b> The connection to AWS is established through a multi-step process that creates a trust relationship between a specific BigQuery connection and an AWS IAM Role.<\/span>8<\/span><\/li>\n<\/ul>\n
\n- IAM Policy Creation:<\/b> First, an administrator creates a standard AWS IAM policy that explicitly defines the permissions BigQuery will have on a specific S3 bucket (e.g., s3:GetObject, s3:ListBucket). This policy can be as granular as necessary, restricting access to specific prefixes within a bucket.<\/span><\/li>\n
- IAM Role Creation:<\/b> Next, an IAM Role is created in AWS. This role is configured to trust a “Web Identity” from accounts.google.com.<\/span><\/li>\n
- BigQuery Connection:<\/b> In the Google Cloud console, a BigQuery connection to AWS is created. This process generates a unique Google identity (a long alphanumeric string) specific to that connection.<\/span><\/li>\n
- Trust Policy Update:<\/b> The administrator then updates the trust policy of the AWS IAM Role, replacing a placeholder with the unique BigQuery Google identity. This final step establishes the trust, allowing only that specific BigQuery connection to assume the role.<\/span><\/li>\n<\/ol>\n
This Web Identity Federation pattern is a security best practice. It uses OpenID Connect (OIDC) tokens for authentication, meaning BigQuery never handles or stores long-lived AWS credentials. The customer retains full control and can revoke access at any time by simply modifying or deleting the IAM policy or role in their AWS account.<\/span>3<\/span><\/p>\n\n- Integration with Azure Active Directory (Microsoft Entra ID):<\/b> A similar principle applies to Azure. BigQuery Omni uses standard Azure Active Directory principals to gain access to data in Blob Storage.<\/span>3<\/span> The integration relies on an OIDC-based setup with Google’s Workforce Identity Federation, which allows Google Cloud services to access Azure resources using federated identities rather than service account keys.<\/span>23<\/span> The customer grants specific roles and permissions to a federated identity within their Azure AD tenant, maintaining sovereign control over their Azure data.<\/span><\/li>\n<\/ul>\n
This federated approach is more secure than alternatives like credential mirroring, but its complexity necessitates careful configuration. A misconfiguration in either the GCP connection or the AWS\/Azure IAM setup could lead to unintended access or service failures.<\/span><\/p>\n\n\n\n| Configuration Step<\/b><\/td>\n | AWS Implementation<\/b><\/td>\n | Azure Implementation<\/b><\/td>\n<\/tr>\n\n| 1. Define Data Access Policy<\/b><\/td>\n | Create an <\/span> | | | | | | | | | | |
![\"\"](\"https:\/\/uplatz.com\/blog\/wp-content\/uploads\/2025\/10\/The-Omni-Cloud-Brain-An-In-Depth-Analysis-of-Google-BigQuery-Omni-as-a-Federated-Analytics-Control-Plane-1024x576.jpg\")
<\/p>\n