3 min readMar 19, 2024


Zhamak Dehghani’s Data Mesh - Monolithic Warehouses and Lakes (Debunked)

Data Analytics Series

In [1] the author categorizes data warehouses (DWHs) and lakes as monolithic architectures, as opposed to data mesh’s distributed architecture, which makes me circumspect about term’s use. There are two general definitions of what monolithic means: (1) formed of a single large block (2) large, indivisible, and slow to change.

In software architecture one can differentiate between monolithic applications where the whole application is one block of code, multi-tier applications where the logic is split over several components with different functions that may reside on the same machine or are split non-redundantly between multiple machines, respectively distributed, where the application or its components run on multiple machines in parallel.

Data Warehouse vs. Data Lake vs. Lakehouse [2]

Distributed multi-tire applications are a natural evolution of the two types of applications, allowing to distribute redundantly components across multiple machines. Much later came the cloud where components are mostly entirely distributed within same or across distinct geo-locations, respectively cloud providers.

From licensing and maintenance convenience, a DWH resides typically on one powerful machine with many chores, though components can be moved to other machines and even distributed, the ETL functionality being probably the best candidate for this. In what concerns the overall schema there can be two or more data stores with different purposes (operational/transactional data stores, data marts), each of them with their own schema. Each such data store could be moved on its own machine though that’s not feasible.

DWHs tend to be large because they need to accommodate a considerable number of tables where data is extracted, transformed, and maybe dumped for the various needs. With the proper design, also DWHs can be partitioned in domains (e.g. define one schema for each domain) and model domain-based perspectives, at least from a data consumer’s perspective. The advantage a DWH offers is that one can create general dimensions and fact tables and build on top of them the domain-based perspectives, minimizing thus code’s redundancy and reducing the costs.

With this type of design, the DWH can be changed when needed, however there are several aspects to consider. First, it takes time until the development team can process the request, and this depends on the workload and priorities set. Secondly, implementing the changes should take a fair amount of time no matter of the overall architecture used, given that the transformations that need to be done on the data are largely the same. Therefore, one should not confuse the speed with which a team can start working on a change with the actual implementation of the change. Third, the possibility of reusing existing objects can speed up changes’ implementation.

Data lakes are distributed data repositories in which structured, unstructured and semi-structured data are dumped in raw form in standard file formats from the various sources and further prepared for consumption in other data files via data pipelines, notebooks and similar means. One can use the medallion architecture with a folder structure and adequate permissions for domains and build reports and other data artefacts on top.

A data lake’s value increases when is combined with the capabilities of a DWH (see dedicated SQL server pool) and/or analytics engine (see serverless SQL pool) that allow(s) building an enterprise semantic model on top of the data lake. The result is a data lakehouse that from data consumer’s perspective and other aspects mentioned above is not much different than the DWH. The resulting architecture is distributed too.

Especially in the context of cloud computing, referring to nowadays applications metaphorically (for advocative purposes) as monolithic or distributed is at most a matter of degree and not of distinction. Therefore, the reader should be careful!

[1] Zhamak Dehghani (2021) Data Mesh: Delivering Data-Driven Value at Scale ( book review)
[2] Databricks (2022) Data Lakehouse ( link)

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IT professional/blogger with more than 24 years experience in IT - Software Engineering, BI & Analytics, Data, Project, Quality, Database & Knowledge Management