Open Lakehouse Architecture

Overview & Architectural Motivation

An Open Lakehouse is a data architecture design that delivers data warehouse reliability, governance, and ACID transactions directly on top of scalable cloud object storage using non-proprietary, open source formats and interface standards.

Traditional architectures forced organizations into binary trade-offs: cheap, unstructured data lakes lacking transaction support and performance, or proprietary cloud data warehouses requiring expensive data ingest, closed storage formats, and complete vendor lock-in.

The Open Lakehouse resolves this dilemma by decomposing the data stack into modular, independent layers:

1. Physical storage format: Immutable columnar files (Apache Parquet).
2. Table format: ACID transaction and metadata layer (Apache Iceberg).
3. Central catalog: Standardized endpoint identity, access control, and credential management (Apache Polaris via the Iceberg REST specification).
4. Compute engines: Independent query and processing engines chosen according to workload (Dremio for sub-second interactive SQL and BI, Spark for batch processing, Flink for streaming, DuckDB for local analytics).
5. Open semantics and agent protocols: Cross-platform semantic models (Apache Ossie) and AI agent tool integration (MCP and A2A).

Architecture & System Flow

Internal Mechanics & Specifications

The mechanics of an Open Lakehouse rely on strict abstraction boundaries between each system layer:

Storage Immutability and Object Store Scalability

Data resides in standard object storage (Amazon S3, Azure Data Lake Storage, or Google Cloud Storage) as Parquet files. Parquet files are written once and never mutated in place.

Table Semantics via Metadata Pointers

Instead of defining tables as directories of files, table state is defined by a tree of metadata files in Apache Iceberg. A write operation creates new Parquet data files, commits updated manifest files, and atomically updates the pointer to a new snapshot root. Readers access consistent snapshots without requiring table-level write locks.

Catalog Independence

Engines discover table snapshot roots by querying an open catalog implementing the Iceberg REST Catalog specification, such as Apache Polaris. The catalog acts as the authoritative authority for access delegation, validating identity and vending time-limited cloud storage credentials directly to the client engine.

Zero-Copy Compute Interoperability

Because the files, metadata, and catalogs use open specifications, multiple engines can query the exact same data simultaneously without ETL duplication. Dremio executes interactive SQL queries, Apache Spark processes nightly aggregation jobs, and Python workloads read snapshots directly through Apache Arrow.

Role in the Open Lakehouse and AI Architecture

The Open Lakehouse acts as the central foundation for both enterprise business intelligence and autonomous AI systems.

By eliminating proprietary file formats and vendor-bound query engines, data engineering teams maintain complete data ownership. Datasets are written once into object storage and made instantly available to analytical dashboards, external partners, and autonomous AI agents.

In modern AI architectures, agentic systems require direct access to fresh, curated enterprise context without waiting for batch data pipelines. When combined with protocols such as MCP (Model Context Protocol) and semantic frameworks such as Apache Ossie, an Open Lakehouse allows AI models to safely execute parameterized SQL queries against live Iceberg tables with full governance, auditing, and fine-grained access control.

Ecosystem Interoperability & Open Interfaces

The Open Lakehouse functions as an interconnected ecosystem of open source technologies:

• Apache Parquet supplies raw columnar encoding and compression.
• Apache Iceberg structures Parquet files into ACID transactional tables with branching and time travel.
• Apache Polaris provides the unified catalog endpoint and multi-engine security enforcement.
• Apache Arrow facilitates high-throughput, zero-copy in-memory exchange between execution nodes and client applications.
• Apache Ossie standardizes shared business metrics across downstream BI tools and AI agents.
• Apache APISIX routes and protects inbound REST and SQL gateway requests across distributed environments.
• MCP and A2A bridge lakehouse data assets to autonomous agent swarms and conversational models.

Production Engineering Considerations

Architecting an Open Lakehouse requires attention to specific operational disciplines:

Small File Management

Frequent streaming writes can generate numerous tiny Parquet files. Regular compaction routines via Iceberg rewrite operations are required to maintain optimal read performance.

Snapshot Retention Policies

Every Iceberg commit produces metadata snapshots. Establish clear retention policies to expire obsolete snapshots and remove orphan files, controlling object storage footprint.

Catalog Centralization

Avoid running divergent catalogs for the same physical datasets. Use a unified catalog implementing the Iceberg REST specification (such as Apache Polaris) to prevent conflicting concurrent writes across heterogeneous compute engines.