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Data residency

As explained in Architecting an Apache Iceberg Lakehouse

Data residency is the requirement that data be stored or processed within specified geographic or jurisdictional boundaries.

Data residency requires data to remain stored or processed within specified geographic or jurisdictional boundaries. It shapes storage topology, replication policies, query planning, encryption, access control, and auditability rather than functioning as a storage-location checkbox. section 5.1.2 section 9.5.6

A residency-aware architecture evaluates:

  • Where primary data and replicas are stored, since public-cloud defaults may span regions while on-premises storage provides greater geographic control. section 5.1.2
  • How encryption, identity integration, access logging, retention, and audit trails support regulatory obligations across storage and retrieval. section 5.1.2 section 7.2.3
  • How catalogs, federation engines, and partitioning enforce locality during metadata management and query planning. section 9.5.6

Hybrid regional storage

A bank uses Amazon S3 for elastic analytics workloads and MinIO clusters in European and US data centers for regulated data. The on-premises clusters provide local control, air-gapped environments, and encryption at rest while supporting residency obligations under GDPR and SEC Rule 17a-4. section 4.5.1

Table 4.1 Stakeholder interview findings

Stakeholder group

Key observations

Challenges highlighted

Data engineers

Fragile, manually intensive pipelines

Complex schema evolution, redundant regional pipelines, and operational overhead

Data analysts and data scientists

Slow and inconsistent access to trusted data

Need for improved data freshness, clarity, and reduced manual preprocessing

Compliance and security teams

Strong regulatory compliance needs around data residency, encryption, and lineage

Lack of built-in auditability and dynamic access controls

Business leadership

Demand for faster insights and lower analytics costs

Desire for new data-driven products is blocked by slow platform agility

IT and infrastructure

Scalability and operational complexity challenges

Hybrid cloud/on-prem needs; difficulties maintaining legacy on-prem resources

Stakeholder findings connect residency requirements with encryption, lineage, auditability, hybrid deployment, and operational risk. section 4.2.1

How Architecting an Apache Iceberg Lakehouse covers it

How deep it goes
Explains placement, replication, encryption, auditing, and query enforcement, but stops short of specifying one universal deployment pattern.
Examples
Uses regional healthcare datasets, jurisdiction-aware Iceberg tables, and hybrid bank storage spanning cloud and on-premises object stores.
What you can do after
Operate/configure storage, catalogs, and query paths so regulated data remains localized and compliance evidence remains auditable.
How it connects
Links data residency with encryption, access control, audit trails, metadata management, hybrid storage, and data governance.

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This explainer was generated by Manning's knowledge graph from the text of Architecting an Apache Iceberg Lakehouse. The section links point to the parts of the book it draws on.