The Data Lakehouse Architecture Guide: Iceberg, Delta, and Hudi

The data lakehouse architecture combines the flexibility of data lakes with the reliability and performance of data warehouses, using open table formats like Apache Iceberg, Delta Lake, or Apache Hudi. In 2026 it has become the default for new data platforms, replacing both traditional warehouses and raw lakes as the architecture of choice.

The data lakehouse architecture in 2026 has become the default approach for new data platforms, replacing both traditional data warehouses and raw data lakes as the architecture of choice. Databricks coined the term, but the lakehouse pattern — combining the flexibility of data lakes with the reliability and performance of data warehouses — has been adopted by every major vendor. The three open table formats that make lakehouses work — Apache Iceberg, Delta Lake, and Apache Hudi — each take different approaches with different trade-offs. This guide explains the lakehouse architecture from the ground up and helps you choose the right table format for your platform.

If you are designing a new data platform or modernizing an existing one, the lakehouse vs. warehouse decision is the most consequential architectural choice you will make. It determines your vendor flexibility, cost structure, AI readiness, and engineering complexity for the next 3-5 years. This guide gives you the depth to make that decision with confidence.

What Is a Data Lakehouse?

A data lakehouse stores data in open file formats (Parquet, ORC) on object storage (S3, GCS, ADLS) while providing warehouse-like capabilities: ACID transactions, schema enforcement, time travel, and SQL query performance. The open table format layer (Iceberg, Delta, or Hudi) is what bridges the gap between raw files and structured tables.

The lakehouse replaces the two-tier architecture (data lake for storage + data warehouse for analytics) with a single tier that serves both purposes. This eliminates the ETL between lake and warehouse, reduces storage costs (object storage is 10-50x cheaper than warehouse storage), and prevents the data divergence that plagues two-tier architectures.

Lakehouse vs. Traditional Architectures

The Three Table Formats: Iceberg vs Delta vs Hudi

The table format is the most important technology decision in your lakehouse architecture. It determines which engines can access your data, how concurrent access is managed, and what features are available.

Apache Iceberg: The Market Standard

Iceberg has won the table format competition (see our detailed Iceberg guide). Every major vendor supports it natively: Snowflake (Iceberg Tables), Databricks (UniForm converts Delta to Iceberg), AWS (Athena, EMR, Redshift Spectrum), Google (BigQuery), Dremio, Starburst, and Cloudera.

For new lakehouse deployments, Iceberg is the default recommendation. Its engine independence means you are not locked into any compute engine. Its partition evolution means you can adapt your partitioning strategy as data patterns change without rewriting data. And its universal vendor support means you can switch engines without migrating table formats.

Delta Lake: The Databricks Choice

Delta Lake is the right choice if Databricks is your primary compute platform and you do not plan to use other engines extensively. Delta is deeply integrated with Spark and Databricks — features like Photon acceleration, Unity Catalog, and Databricks SQL are optimized for Delta tables.

The UniForm feature (which automatically maintains Iceberg-compatible metadata alongside Delta metadata) mitigates the lock-in concern. With UniForm, external engines can read Delta tables through the Iceberg interface. However, this adds complexity and the Iceberg compatibility layer may lag behind native Iceberg features.

Delta Lake is also the most mature format for streaming workloads on Spark Structured Streaming. If your primary use case is real-time analytics on Databricks, Delta provides the best integrated experience.

Apache Hudi: The Streaming Specialist

Hudi was designed at Uber for high-volume incremental data processing. Its merge-on-read architecture and record-level indexing make it excellent for use cases with high update rates — CDC pipelines, real-time event processing, and tables with frequent row-level modifications.

However, Hudi's community momentum has slowed relative to Iceberg and Delta. Vendor adoption is narrower, and the feature gap with Iceberg has widened. For new deployments, Hudi is only recommended for specific streaming-heavy use cases where its merge-on-read performance advantage over Iceberg is measurable and significant.

Lakehouse Architecture Patterns

A production lakehouse typically follows a medallion architecture with three layers:

• •Bronze (raw): Raw data as ingested from source systems. Minimal transformation — just format conversion and metadata tagging. Schema-on-read. Retained indefinitely for reprocessing.
• •Silver (cleaned): Deduplicated, validated, and standardized data. Business keys resolved, data types enforced, obvious quality issues fixed. This is where most data quality checks run.
• •Gold (business): Business-level aggregations, metrics, and dimensional models optimized for consumption. This layer serves dashboards, AI agents, and applications.

Each layer is a set of Iceberg (or Delta/Hudi) tables in object storage. Transformations between layers are managed by dbt, Spark, or your preferred transformation engine. The open table format ensures that any engine can read any layer — analytics engines query gold tables, ML pipelines read silver tables, and data engineers debug issues by time-traveling through bronze snapshots.

Lakehouse + AI: Why Architecture Matters for AI Agents

The lakehouse architecture has a specific advantage for AI workloads: open table formats expose metadata that AI agents can use to understand your data. Iceberg manifests contain column statistics, partition information, and schema history that an AI agent can read to make intelligent query decisions without scanning data.

Data Workers leverages this metadata extensively. Its 15 MCP-native agents read table format metadata to provide AI agents with automatic understanding of data structure, distribution, freshness, and schema evolution — without manual documentation. This makes the lakehouse architecture inherently more AI-ready than traditional warehouses where metadata is locked behind proprietary APIs.

Being open-source under Apache 2.0, Data Workers integrates with all three table formats and 85+ other data tools. Teams building lakehouses report saving over $1.3M annually by combining open table formats with open-source observability and governance instead of commercial alternatives.

Getting Started: Your First Lakehouse

• •Choose Iceberg unless you have a specific reason for Delta or Hudi. Iceberg provides the broadest compatibility and the lowest risk of lock-in.
• •Start with a REST catalog for maximum engine flexibility. Migrate to a vendor-specific catalog later if needed.
• •Implement the medallion architecture from day one. Even if you only have bronze and gold initially, the three-layer pattern prevents shortcuts that cause problems at scale.
• •Add a context layer early. Metadata from your table format + semantic definitions from a context layer = AI-ready data infrastructure.
• •Monitor from the start. Deploy quality monitoring (Elementary, Data Workers, or Soda) before you have quality problems, not after.

Explore the Data Workers documentation for lakehouse architecture guides and integration patterns.

Building a data lakehouse? Book a demo to see how Data Workers provides the context layer, observability, and AI agent grounding that makes your lakehouse architecture production-ready.