Data Lake, Data Warehouse, and Lakehouse: Concepts, Architectures, and Industry Practices

The article introduces the rapid development of big‑data technologies over the past decade, emphasizing that massive, diverse data storage and computation are valuable business assets. Companies such as Snowflake and Databricks have built market‑leading cloud data‑warehouse and lake‑house solutions, prompting major cloud providers to launch their own data‑lake, data‑warehouse, and lake‑house products.

It explains that many data‑lake and data‑warehouse terms are coined to describe emerging needs rather than strict mathematical definitions. Users should understand these concepts from a demand‑driven perspective, focusing on the ability to ingest, store, and compute on large, heterogeneous datasets.

The article distinguishes data lakes and data warehouses: data lakes excel at ingesting massive, varied data and supporting concurrent writes, while data warehouses provide optimized storage structures and high‑performance query engines for analytical workloads. In practice, both aim to deliver information from the data, and the choice depends on data volume and complexity.

A visual data‑flow diagram shows how analysts perform business modeling, data engineers design and maintain data architectures, and end‑users derive value from business and data models.

It then asks why the industry is moving toward lake‑house integration, describing a lake‑house as a system that combines the openness of a data lake with the performance and management features of a data warehouse. By organizing data at ingestion time and providing standardized read interfaces, lake‑house architectures enable both batch and streaming processing while improving query performance.

The article outlines typical lake‑house implementations: hot data resides in a highly optimized warehouse for fast queries, while cold data is stored in a lake with lower cost. Queries can transparently access cold data via the warehouse’s compute layer, often using elastic compute nodes for on‑demand processing.

Several industry solutions are listed, including:

Alibaba Cloud MaxCompute + Hologres

Alibaba Cloud EMR + StarRocks

Huawei Cloud lake‑house

ByteDance (Doris‑based lake‑house)

ByteDance Volcano Engine lake‑house service

Bilibili lake‑house architecture

Google BigLake

Amazon Lake House

Azure Lake House

SnowFlake Data Lake

The concluding summary states that lake‑house architectures address scenarios with extremely large and diverse datasets, offering high‑speed analytics (warehouse) and scalable storage (lake) while simplifying overall system complexity.

Personal evaluations suggest SnowFlake provides the most mature lake‑house for analytical workloads, Doris/StarRocks have strong potential, and Spark/Presto‑based solutions are suitable for complementary use cases.