Federated Queries Across Sovereign Clouds: A Practical Playbook

Hook — your analytics are blocked by borders. Here’s how to fix that without breaking compliance.

Teams complain that analytics across regions is slow, expensive, and legally risky. You can stop copying data, stop building brittle pipelines, and still respect data residency rules by implementing robust federated queries that span the new AWS European Sovereign environment and other clouds/regions. This playbook gives a practical step‑by‑step implementation path for 2026: architectures, concrete controls, and operational checks that satisfy legal and engineering requirements.

Executive summary — what you’ll get from this playbook

• A repeatable architecture for running federated analytical queries that keeps data in sovereign boundaries while enabling cross‑region analytics.
• Concrete choices for engines and connectors, with tradeoffs for performance, cost, and control.
• Prescriptive controls for encryption, policy enforcement, and trusted connectors that satisfy EU sovereignty and typical legal constraints.
• Step‑by‑step implementation and validation checks, plus operational runbooks for auditing, cost control and troubleshooting.

Context — why 2026 changes the calculus

Late 2025 and early 2026 accelerated the move to sovereign clouds. In January 2026 AWS launched the AWS European Sovereign Cloud, an environment designed to be physically and logically separate from other AWS regions to meet EU digital sovereignty requirements. Regulators and customers increasingly expect cryptographic and legal assurances that keys, telemetry and control planes remain within jurisdictional boundaries.

At the same time, analytics platforms and open source engines (Trino/Starburst, Dremio, ClickHouse, etc.) improved federated query capabilities and connector ecosystems. These developments make it practical to run federated analytical queries where compute can be split, pushed down and executed under sovereign constraints — if you design the trust model and enforcement controls correctly.

High-level patterns for federated queries across sovereign clouds

There are three practical patterns you will choose from depending on policy and performance needs:

1. In‑region connector execution (preferred for strict sovereignty) — run the connector and any query fragments that touch sovereign data inside the sovereign cloud. The control plane can be remote, but data and related compute run under the sovereign boundary.
2. Remote federation with strict policy enforcement — run the federated engine outside the sovereign cloud but enforce policies using remote attestation, encrypted execution (confidential computing), and an auditable connector that only returns aggregated or masked results.
3. Data virtualization with materialized slices — for heavy queries, create small pre‑approved materialized views or aggregates in the sovereign cloud, accessible to external analytics tooling while keeping raw data resident.

Key components in your architecture

• Federated query engine (Trino/Starburst, Dremio, Presto, ClickHouse federated options) — orchestrates queries and pushdown.
• Trusted connector — containerized connector deployed inside the sovereign cloud with signed images and runtime attestation.
• Control plane — metadata/catalog, orchestrator, and query planner. Can be outside sovereign cloud if it never receives raw data or keys.
• Data plane — storage (S3 or compatible), KMS/HSM, CloudTrail/Logs, all resident in the sovereign region.
• Network controls — VPCs, PrivateLink, Endpoint policies, and cross-account roles for least privilege access.
• Policy engine — ABAC/row‑level security (RLS), column masking, tokenization, and query gating.

Step‑by‑step implementation playbook

Step 0 — Preflight: legal and regulatory alignment

Before implementation, involve your legal, privacy (DPO) and compliance teams. Capture requirements: which data classes cannot leave jurisdiction, required logging retention, key location, and approved data transformations. Document these as enforceable policy artifacts used by technical gates in later steps.

Step 1 — inventory and classification

1. Inventory datasets and map to residency constraints (e.g., personal data, IP, regulated sector data).
2. Classify each dataset: resident only, resident with anonymized/aggregated export allowed, or open.
3. Tag assets (S3 objects, tables) using a standardized metadata schema (region, sensitivity, retention, legal owner).

Step 2 — define trust model and data flows

• For each dataset, declare whether compute can run in a remote control plane or must execute inside the sovereign data plane.
• Define trusted connector responsibilities: authentication, attestations, pushing query compute, masking, and encrypted result handling.
• Document the authorized principals that can request federated queries and the allowed output forms.

Step 3 — choose your federation engine and connector topology

Selection criteria: connector maturity, pushdown capabilities, ability to run connectors in the sovereign cloud, access control features, and cost model.

• Trino / Starburst — excellent pushdown, broad connector ecosystem, commonly used for S3 and database federation. Ideal when you can run connectors in‑region or deploy a lightweight Trino worker inside the sovereign cloud.
• Dremio — strong virtualization and reflection (materialized views) options to reduce cross‑region compute and cost.
• ClickHouse — growing OLAP capabilities and external table federation; useful for high‑throughput slices inside the sovereign cloud.

Recommended topology: run a lightweight connector/worker set inside the AWS EU Sovereign region with a control plane outside. The control plane orchestrates queries but never has access to raw data or keys.

Step 4 — authentication and authorization

Implement least‑privilege cross‑account access patterns.

• Use cross‑account IAM roles with strict trust policies (external IDs), or OIDC federation for workloads that need STS. Ensure the role permissions only allow the minimal S3/KMS actions.
• Adopt ABAC to centralize policy decisions: include dataset tags, requester attributes, and legal consent attributes.
• Enforce row‑level security and column masking at the connector or storage layer; never rely solely on client‑side filtering.

Step 5 — encryption and key management

Encryption is non‑negotiable. Ensure keys and key policies meet sovereignty constraints.

• Use cloud KMS keys that are region‑bound and cannot be exported. For AWS EU Sovereign, provision KMS keys in the sovereign account/region and restrict key usage to the connector IAM principals.
• Consider BYOK or dedicated HSM (AWS CloudHSM or external HSM) if your legal team requires exclusive control of key material.
• Ensure TLS 1.2+ for all inter‑component communications and use mutual TLS for connector control channels where feasible.

Step 6 — deploy trusted connectors

Trusted connectors are the linchpin. They execute data access under the sovereign boundary, perform policy enforcement, and return only authorized artifacts.

• Containerize connector code and push signed images to a sovereign container registry. Use Sigstore or image signing and verify signatures at startup.
• Use attestation: the connector should present a cryptographic attestation to the control plane proving it runs inside the approved sovereign environment (e.g., instance identity documents, signed boot measurements, or confidential compute attestations).
• Harden the runtime: minimal OS image, read‑only filesystem for connectors, strict network egress policies, and automated vulnerability scanning in the build pipeline.

Step 7 — network design and isolation

Make networking restrictive by default.

• Use VPC endpoints (S3 Gateway/Interface) and AWS PrivateLink to avoid public internet egress for data access.
• Apply endpoint policies limiting what S3 buckets and prefixes are accessible from the connector.
• Use Transit Gateway or VPC peering only where necessary, and log/VPC flow‑log everything into the sovereign log store.

Step 8 — enforce policy and data transformations

Apply enforcement at the earliest possible point (the connector) to reduce leakage risk.

• Implement RLS/column masking in the connector or in the storage layer (e.g., S3 object encryption with per‑column encrypted fields, or using a query engine's masking policies).
• Return only aggregated, anonymized or tokenized results when required by policy. Keep a registry of allowed export transforms for each dataset.
• Log every access and mask sensitive fields in logs where required.

Step 9 — cost control & performance optimization

Federation can be expensive if it moves large amounts of data. Optimize query pushdown and caching.

• Prefer predicate pushdown and projection pushdown so only required columns/rows flow out of the sovereign region.
• Use reflections/materialized views inside the sovereign cloud for heavy, repetitive queries. These are cheaper and reduce cross‑region compute.
• Implement per‑query cost control and limiters on the control plane. Reject or require approval for queries that exceed cost or data access thresholds.

Step 10 — observability, auditing and profiling

Observability is essential for compliance and performance debugging.

• Collect and store audit trails inside the sovereign region: CloudTrail, connector logs, query plans and explain outputs. Ensure retention meets legal obligations.
• Enable query profiling to identify expensive operators, missing pushdowns and high data egress. Use these signals for automated query rewrites or to request user adjustments.
• Expose limited, redacted telemetry to the control plane for global monitoring but keep raw logs in the sovereign store.

Step 11 — validation, testing and compliance checks

1. Run privacy and legal test suites: assert that no raw PII exits the sovereign region for resident‑only datasets.
2. Perform red team tests and data exfiltration scenarios to validate network and policy enforcement.
3. Automate compliance checks in CI/CD for connector image signatures, runtime attestation and KMS policies.

Step 12 — operational runbook and incident response

Create runbooks for these common incidents:

• Connector compromise — isolate connector, rotate keys, and redeploy signed images.
• Unauthorized query — revoke requester permissions, preserve audit logs, and perform a legal review.
• Performance regression — capture query explain, compare against baseline, and enable reflection or caching.

Concrete example: federated analytics with Trino and AWS European Sovereign Cloud

Scenario: analytics team in non‑EU region needs aggregated metrics from EU resident logs stored in the AWS European Sovereign Cloud. Raw logs cannot leave the sovereign region.

1. Deploy a Trino connector worker group inside the AWS EU Sovereign VPC. The connector has an IAM role allowing S3 GetObject and KMS Decrypt for keys in the sovereign account only.
2. Sign and attestate the connector image; verify attestation on boot against the control plane’s allowlist.
3. Control plane (outside EU sovereign) submits SQL. The planner determines which fragments can run outside and which must run inside EU sovereign. All scans of resident logs are executed by the in‑region worker.
4. The in‑region worker performs necessary masking and aggregation and returns only the aggregated result set over a mTLS channel. Only aggregated results (no raw rows) leave the sovereign region.
5. All access and query plans are logged to sovereign CloudTrail; a copy of metadata (non‑PII) is sent to the control plane for cost accounting.

This pattern keeps keys and raw data in the sovereign environment, limits egress to aggregated outputs, and provides full auditing and attestation.

Operational tips and anti‑patterns

• Anti‑pattern: moving raw data out of the sovereign region because it’s “easier.” Always evaluate materialized slices/aggregates first.
• Tip: instrument a budget guardrail per user/group to prevent runaway cross‑region queries that are expensive.
• Tip: automate connector signing and attestation as part of CI/CD. Manual processes fail at scale.
• Anti‑pattern: trusting the control plane without proof. Always require attestation from in‑region connectors before accepting results.

Technology selections — quick reference

• Federation engine: Trino/Starburst for broad connectors and pushdown; Dremio for virtualization and reflections; ClickHouse for high‑throughput in‑region OLAP.
• Network controls: AWS PrivateLink, VPC Endpoints, Endpoint Policies, Transit Gateway.
• Key management: region‑bound KMS keys, CloudHSM or customer HSM for strict control.
• Attestation & signing: Sigstore, AWS Nitro Enclaves attestations, OCI image signing.
• Policy enforcement: native engine RLS, connector masking, or an external policy engine (OPA/Rego) integrated into the connector path.

2026 trends and future directions — what to watch

Expect three rapid developments through 2026:

1. More sovereign cloud launches and certifications — expect additional region‑specific offerings and regulatory guidance that will simplify some legal questions but increase heterogeneity.
2. Built‑in confidential computing for federated engines — cloud vendors and processors will make attestable, encrypted remote compute more mainstream, enabling safer remote execution across borders.
3. Standardized trusted connectors and attestation flows — common standards for connector signing/attestation (Sigstore plus hardware attestation) will make audits simpler and deployments more repeatable.

Practical rule: keep raw data where the law mandates; move compute to the data or provide certifiable transformations (aggregates, anonymization) as the export path.

Actionable takeaways

• Design for in‑region connector execution when data residency is strict.
• Use signed, attested connectors with limited network egress and region‑bound keys.
• Prefer pushdown and in‑region materialized views to reduce cross‑region egress and cost.
• Automate compliance checks and attestation verification in CI/CD and at runtime.
• Keep observability and raw audit logs inside the sovereign region; share only redacted metadata externally.

Checklist — deployable validation steps

1. Dataset classification completed and tagged.
2. Legal requirements documented and turned into policy artifacts.
3. Connector images signed and attestation enabled.
4. Key policies and KMS keys provisioned in sovereign region with limited principals.
5. Endpoint policies restricting S3 prefixes created.
6. RLS/masking rules implemented and tested.
7. Cost control and query approval workflows enabled.
8. Audit logs retained in sovereign store for required retention period.

Final notes — risk, governance and the human element

Technical controls are necessary but not sufficient. Governance, training, and legal processes close the loop. Ensure data owners approve export transforms, and that analysts understand what they can request. Build an approval workflow for any query that needs a non‑standard export of resident data.

Call to action

If you’re designing cross‑region analytics with sovereignty constraints, start with a controlled pilot: pick a single dataset, deploy a trusted connector in the AWS European Sovereign Cloud, and validate the full attestation and audit lifecycle. Want a downloadable checklist, connector reference configs, and a prebuilt Trino worker manifest for the AWS EU Sovereign Cloud? Contact our team for a tailored assessment and pilot blueprint.

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