How to Architect an On-Premises Data Lakehouse

An on-premises data lakehouse applies modern lakehouse architecture within privately owned or leased data centers, offering organizations data sovereignty, regulatory control, lower local latency, and avoidance of cloud egress costs without relying on Hadoop. The recommended core stack combines S3-compatible object storage, open Parquet or ORC files, Apache Iceberg for ACID transactions and schema evolution, and Trino as a distributed SQL engine, while separating storage and compute so each can scale independently. Effective implementations require careful planning for storage capacity, durability, network bandwidth, catalog interoperability, query caching, workload isolation, Iceberg maintenance, governance, and recovery procedures. Federated queries can access on-premises, cloud, and SaaS sources without immediately copying data, with frequently used paths later materialized into Iceberg tables. Security should incorporate fine-grained access controls, auditing, and governed data products, particularly for AI workloads that must keep data and inference within organizational boundaries. For availability, organizations can use coordinator failover, multi-cluster routing, and fallback mechanisms, though preview features and licensing requirements should be assessed carefully. Migration from Hadoop is best handled incrementally by first replacing Hive or Impala with Trino, introducing object storage and new Iceberg tables, selectively converting valuable Hive tables, and retiring legacy components after users transition.

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