Data Lake vs Data Warehouse vs Lakehouse

Data lake, data warehouse and lakehouse are three answers to one question: where should an organisation keep the data it analyses? They differ in what they store, when structure is imposed, and who can use the result. The terms are often used loosely, so it helps to pin each down before comparing.

Data warehouse: structured and curated

A data warehouse is a central repository of structured data, organised into relational schemas for reporting, analysis and BI. Structure is enforced as data is loaded, known as schema-on-write: before a row lands, its columns, types and often its quality rules are already defined. That makes warehouses fast and predictable for SQL and dashboards, and gives everyone the same definitions. The costs are rigidity, since new kinds of data need modelling before they can be loaded, and poor fit for images, audio, free text and the large raw datasets that machine learning uses. Data warehouses explained covers the model in depth.

Data lake: everything, in its raw form

A data lake is a storage repository that holds large volumes of data in its native, raw format: relational extracts, JSON and logs, images and video (Microsoft Learn: data lake). Structure is applied when the data is read, schema-on-read, so anything can be stored now and interpreted later. Storage is cheap object storage, and compute engines such as Spark process it at scale.

The strengths are flexibility and cost: raw data is kept for uses not yet imagined, and data scientists can explore it directly. The weaknesses are the mirror image. Microsoft's guide lists data quality and governance, integrity risks from incomplete ingestions, performance at scale, and discoverability, warning that without cataloguing a lake can devolve into a data swamp, where valuable information is present but inaccessible or misunderstood. A folder of files also has no transactions: a failed job can leave half-written data that readers then pick up.

Lakehouse: warehouse features on lake storage

For years the standard design used both: a lake for raw data and data science, and a warehouse, loaded from the lake, for BI. That works, but it means two copies of the data, two security models and pipelines between them.

A lakehouse aims to remove the split. Databricks defines it as a data management system that combines the benefits of data lakes and data warehouses (Databricks docs: lakehouse). Data stays in low-cost cloud storage in open formats such as Parquet (Microsoft Learn: big data architectures), and two layers are added on top:

• A table format, such as Delta Lake or Apache Iceberg, which adds ACID transactions, schema enforcement and time travel to files; Delta Lake explained shows how, and Iceberg vs Delta Lake vs Hudi compares the options.
• A catalogue and governance layer, which presents files as tables and controls access, lineage and discovery.

With those in place, SQL analytics, BI, data engineering and machine learning can all run on one copy of the data.

Side by side

How to choose

• Mostly structured business data, SQL-skilled team, BI as the main use: a cloud data warehouse is the simplest path; Snowflake explained looks at one.
• Large volumes of varied or unstructured data, data science and machine learning as major uses: a lakehouse, so models and reports share data.
• A data lake alone suits archiving and exploration, but anything published to the business needs the guarantees a warehouse or table format provides.
• Already running a lake and a warehouse? There is no need to rebuild. Adding a table format to the lake and querying it from the warehouse narrows the gap gradually.

The distinction is also blurring from both sides. Warehouses now read open table formats in object storage, and lakehouse platforms offer warehouse-style SQL engines. For new platforms the practical decision is less "lake or warehouse" than which engine, which table format and how much the team wants managed for them.

Whatever you choose, layer it

All three benefit from the same internal organisation: raw data kept as received, a cleaned and validated layer, and curated outputs for specific uses. Microsoft's data lake guide describes exactly these layers, raw, cleansed and curated, as the medallion architecture; medallion architecture builds it on real data. Structure, tests and ownership are what separate a platform from a swamp, far more than the name on the architecture diagram.

Data lake versus data warehouse, data warehouse concepts and a data lake implementation project are part of the Data Engineering course.

Sources

• What is a data lake? (Azure Architecture Center, Microsoft Learn) — Microsoft Learn
• What is a data lakehouse? (Databricks documentation) — Databricks
• Big data architectures (Azure Architecture Center, Microsoft Learn) — Microsoft Learn