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Hybrid deployment

As explained in Architecting an Apache Iceberg Lakehouse

A hybrid cloud deployment combines infrastructure operated on premises with services or storage provided by public clouds.

Hybrid deployment combines on-premises infrastructure with public-cloud services or storage. It connects regulated or legacy systems to cloud-native analytics while balancing data residency, scalability, cost, and operational complexity. section 4.2.1 section 8.4

A practical hybrid architecture typically addresses these concerns:

  • Keep regulated datasets in regionally bound on-premises storage while exposing S3-compatible APIs for query and ingestion integration. section 4.4.1
  • Use a storage-independent federation layer such as Trino to query data distributed across on-premises systems, clouds, or multiple cloud vendors. section 8.3.7
  • Plan for connector configuration, workload tuning, security integration, caching, and consistent governance across federated sources. section 8.3.7
  • Trade infrastructure control and regulatory alignment against higher operational expertise and complexity than managed cloud services require. section 8.3.7 section 8.4

Regulated banking analytics

A bank can retain EU-regulated data on premises, connect it to cloud-native analytics, and preserve queryability for archived data through Iceberg metadata. This approach addresses residency and audit needs while reducing dependence on inflexible on-premises scaling. section 4.2.1 section 4.4.1

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

Use the stakeholder findings to compare the compliance, scalability, access, cost, and operational pressures that motivate hybrid deployment. section 4.2.1

How Architecting an Apache Iceberg Lakehouse covers it

How deep it goes
Explains residency, storage interfaces, federation, and operational tradeoffs but stops short of detailed deployment configurations.
Examples
Uses regulated banking data, EU on-premises storage, cloud analytics, Trino federation, and archival tiering as concrete examples.
What you can do after
Apply it via Trino to query distributed on-premises and cloud data while preserving infrastructure and residency constraints.
How it connects
Ties data residency, S3-compatible storage, Iceberg metadata, data federation, Trino, governance, and managed services together.

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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.