Data Lakehouse Architecture Diagrams

Visualize data lakehouse systems — object storage, open table formats (Iceberg, Delta Lake), catalog layers, and multi-engine compute (Spark, Snowflake, Trino) unifying batch and streaming analytics. Generate accurate lakehouse architecture diagrams from plain English in seconds.

What is a data lakehouse architecture?

A data lakehouse architecture combines an open table format — such as Apache Iceberg or Delta Lake — layered on top of cheap object storage (S3, ADLS, GCS) to deliver warehouse-like ACID transactions, schema enforcement, and time travel while keeping storage and compute fully decoupled. A plain data lake stores raw files with no transactional guarantees or enforced schema, which makes it prone to becoming an unreliable "data swamp." A traditional data warehouse gives you those guarantees but locks storage and compute together in a single proprietary engine.

The lakehouse pattern closes that gap: any number of query engines can read and write the same underlying table files concurrently, using a shared catalog to agree on the current table state, so teams get warehouse reliability without giving up the flexibility and cost profile of object storage.

Key components to diagram

Object storage and open table format

The foundation layer is cloud object storage (S3, ADLS Gen2, GCS) holding data files, typically Parquet, organized by an open table format — Apache Iceberg, Delta Lake, or Apache Hudi. The table format adds a metadata layer of manifests and snapshots on top of the raw files, giving ACID transactions, schema evolution, and time travel. Diagram this as the base node that every compute engine reads from and writes to.

Catalog / metastore layer

The catalog tracks which tables exist and which snapshot of each table is currently active, so multiple engines see a consistent view of the data. Common catalog implementations include the Hive Metastore, AWS Glue Data Catalog, Unity Catalog (Databricks), and the Iceberg REST catalog. Show the catalog as a shared service that every compute engine queries before reading table files directly from object storage.

Multi-engine compute

Because the table format and catalog are open, multiple engines can operate on the same tables without copying data: Spark for large-scale ETL and machine learning, Trino or Presto for interactive SQL, Snowflake or Databricks SQL warehouses for BI workloads. Diagram each engine as a separate node connecting to the shared catalog and object storage, and call out any engine-specific optimizations like caching layers or materialized views.

Streaming ingestion (CDC) into the lakehouse

Change data capture tools (Debezium, Fivetran, or a managed connector) stream row-level inserts, updates, and deletes from operational databases into a message bus like Kafka or Kinesis, which a streaming job (Spark Structured Streaming, Flink) then merges into the lakehouse tables using upsert operations supported by the open table format. Show the CDC source, streaming layer, and merge job as a pipeline feeding into the same tables that batch jobs write to.

Lakehouse platforms (2026)

Databricks: Lakehouse platform built around Delta Lake, with Unity Catalog for governance and Photon for accelerated SQL and Spark execution
Snowflake: Cloud data warehouse with native support for Iceberg tables, letting Snowflake compute query data stored in your own object storage under an open format
Apache Iceberg + Trino: Fully open-source stack — Iceberg tables on S3 or HDFS queried by Trino for interactive SQL and Spark for batch processing, with no vendor lock-in
AWS S3 + Athena/Glue: Serverless lakehouse on AWS — Glue Data Catalog tracks table metadata, Athena runs SQL queries directly against S3-backed Iceberg or Parquet tables with no cluster to manage

Example prompt

"Data lakehouse architecture for a data platform team using Databricks with a medallion architecture. Bronze layer: raw events land in Delta Lake tables on S3, ingested via Auto Loader from Kafka topics and daily batch exports from Postgres using Fivetran. Silver layer: a Spark job cleans, deduplicates, and conforms bronze tables to a shared schema, writing validated Delta tables partitioned by date. Gold layer: aggregation jobs build business-level star schema tables (fact_orders, dim_customers) optimized for BI queries. Unity Catalog governs access across all three layers and tracks lineage from bronze to gold. Databricks SQL warehouses serve gold tables to Tableau dashboards, while a separate Trino cluster queries the same Delta tables for ad hoc analyst queries. Show the bronze/silver/gold flow left to right, with Unity Catalog as a governance layer spanning all three, and the two independent query engines branching off the gold layer."

Frequently asked questions

What is the difference between a data lake and a data lakehouse?

A data lake is raw files in object storage with no built-in transactional guarantees, so concurrent writes, schema changes, and partial failures can corrupt data or produce inconsistent reads. A data lakehouse adds an open table format (Iceberg, Delta Lake) on top of that same object storage, which introduces ACID transactions, schema enforcement and evolution, and time travel — giving you the reliability of a data warehouse while keeping the low-cost, engine-agnostic storage of a data lake.

Should I choose Iceberg or Delta Lake?

Delta Lake is the most seamless choice if you are standardized on Databricks — it has the deepest integration with Unity Catalog, Photon, and Databricks-native tooling. Apache Iceberg is a better fit if you need broad, vendor-neutral engine support, since it has wide native support across Snowflake, Trino, Spark, Flink, and AWS Athena/Glue. Both support ACID transactions, schema evolution, and time travel; the deciding factor is usually which engines you need to query the same tables with.

Can multiple compute engines share the same lakehouse tables?

Yes — that is the core advantage of the lakehouse pattern. As long as every engine reads and writes through the same open table format and agrees on the current table state via a shared catalog (Glue, Unity Catalog, Hive Metastore, or an Iceberg REST catalog), engines like Spark, Trino, Snowflake, and Databricks SQL can all query and update the same underlying tables without copying data between systems.

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