Glossary
What is data lakehouse?
A data lakehouse is a data platform that combines the storage of a data lake with the table structure and governance of a data warehouse. Open table formats such as Delta Lake and Apache Iceberg make this possible.
Data lakehouse explained
In a lakehouse the data sits as files in object storage, usually in the Parquet format. An open table format such as Delta Lake or Apache Iceberg adds a layer on top with transactions, schemas and version history, so those files behave like database tables.
The idea is that you no longer need two copies, a lake for raw data and a warehouse for reporting, but one store that different compute engines work on. Microsoft Fabric, Databricks and Snowflake all offer variants of this model.
A lakehouse is mainly worthwhile with large data volumes or lots of raw data. For a mid-sized organisation with a few source systems, a classic data warehouse is often simpler to manage and just as suitable.
Example
A larger sister company of Acme Groothandel stores web shop click data and order data together in a lakehouse. Data scientists work on the raw tables, while the finance team uses a structured layer for monthly reporting.
Data lakehouse in ENABLE
ENABLE has no integration with a lakehouse or with Microsoft Fabric; ENABLE's data features read from a PostgreSQL data warehouse per customer. Data from a lakehouse can be loaded into that warehouse outside the portal.
FAQ
Frequently asked questions
What is the difference between a data lakehouse and a data warehouse?
A data warehouse stores data in a database with its own storage, while a lakehouse stores tables as files in a data lake with an open table format on top. A lakehouse combines the flexibility of a lake with the structure of a warehouse.
What is Delta Lake?
Delta Lake is an open table format that extends Parquet files with transactions, schemas and version history. It is the default format in Microsoft Fabric and Databricks, among others.
Does every organisation need a lakehouse?
No. With a limited number of source systems and structured data, a data warehouse is often simpler and cheaper to manage.
Further reading
- ResourcesWhat is a data warehouse? Explained and comparedA data warehouse is a central database in which you bring together data from different source systems, such as your ERP, CRM and webshop, clean it up and organise it so that you can report on it and analyse it effectively. A source system is built to process transactions; a data warehouse is built for reading, comparing and keeping history. A data lake stores raw data in any format, and a data lakehouse combines the storage of a lake with the structure of a warehouse.Read more
- SolutionMicrosoft Fabric: what it is and when it fitsWhat Microsoft Fabric is, when it fits and how it relates to ENABLE.Read more
- SolutionBuild a data platform: layers, choices and approachFrom sources to dashboards and AI: how a data platform is structured.Read more
- ResourcesGlossary of data, AI and Power BI termsWhat is a service principal, row-level security, a semantic model, text-to-SQL or RAG? Short, clear definitions of terms around data, Power BI and AI.Read more
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