Is a lakehouse just a data lake with Parquet files?

No. Parquet files in object storage are only the storage substrate. The lakehouse behaviour comes from a table format and catalogue that define schemas, table locations, snapshots, transactions, and permissions. Without that metadata layer, engines see a loose collection of files and concurrent writes, schema changes, and governance become much harder.

When should a warehouse still be used?

Use a warehouse when the priority is polished SQL performance, workload isolation, semantic modelling, fine-grained governance, and predictable business reporting. A lakehouse can store and prepare shared datasets, while a warehouse serves high-value analytical experiences. The split depends on latency expectations, user skills, governance needs, and cost tolerance.

What does the catalogue do in a lakehouse?

The catalogue is the shared map between engines and data. It tells tools which tables exist, where their data and metadata live, what schemas they expose, and often which users may access them. Without a reliable catalogue, each engine may interpret storage differently, which leads to inconsistent results and weaker governance.

Lakehouse Architecture Overview

Lakehouse architecture is a data platform design that keeps data in scalable object storage, adds table-format metadata for reliability, and lets different compute engines query or transform the same datasets. It aims to combine data lake flexibility with warehouse-like tables, governance, and analytical behaviour.

The problem it addresses is that raw data lakes are cheap and flexible but can become unreliable piles of files, while traditional warehouses are easier to query but often couple storage, compute, and proprietary management tightly. Engineering teams need somewhere to land logs, events, extracts, and curated data without forcing every workload through one engine or copying data into a separate system for each use case.

A lakehouse usually starts with object storage such as S3, ADLS, or GCS. Data is stored as files, commonly columnar files for analytics. A table format such as Delta Lake, Apache Iceberg, or Apache Hudi records which files belong to a table, its schema, partitions, snapshots, and transaction state. Compute engines then consult that metadata, read the relevant files, and write new table versions safely rather than treating storage as an unmanaged folder.

The trade-off is that the architecture has more moving parts. You must operate or choose a catalogue, select table formats, manage permissions consistently, and understand which engines support which features. Performance also depends on file layout, partitioning, compaction, caching, and the query engine, not just on the word lakehouse. It is commonly misunderstood as a product category; in practice it is a set of separations and contracts between storage, metadata, and compute.

Engineers meet lakehouse architecture when designing ingestion pipelines, analytics platforms, or machine learning data stores. Spark might transform raw events into curated tables, Trino or Athena might serve interactive SQL, and a warehouse may still hold governed reporting models. The honest design question is not whether a lakehouse replaces a warehouse, but which workloads benefit from open shared storage and which need a specialised warehouse experience.

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Common questions

Is a lakehouse just a data lake with Parquet files?: No. Parquet files in object storage are only the storage substrate. The lakehouse behaviour comes from a table format and catalogue that define schemas, table locations, snapshots, transactions, and permissions. Without that metadata layer, engines see a loose collection of files and concurrent writes, schema changes, and governance become much harder.
When should a warehouse still be used?: Use a warehouse when the priority is polished SQL performance, workload isolation, semantic modelling, fine-grained governance, and predictable business reporting. A lakehouse can store and prepare shared datasets, while a warehouse serves high-value analytical experiences. The split depends on latency expectations, user skills, governance needs, and cost tolerance.
What does the catalogue do in a lakehouse?: The catalogue is the shared map between engines and data. It tells tools which tables exist, where their data and metadata live, what schemas they expose, and often which users may access them. Without a reliable catalogue, each engine may interpret storage differently, which leads to inconsistent results and weaker governance.

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