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Continuing the ideas on Christopher Laubenthal's article "Why one person can't do everything in the data space" [1] and why his analogy between a college's functional structure and the core data roles is poorly chosen. In the last post I mentioned as a first argument that the two constructions have different foundations.
Secondly, it's a matter of construction, namely the steps used to arrive from one state to another. Indeed, there's somebody who builds the data warehouse (DWH), somebody who builds the ETL/ELT pipelines for moving the data from the sources to the DWH, somebody who builds the sematic data model that includes business related logic, respectively people who tap into the data for reporting, data visualizations, data science projects, and whatever is still needed in the organization. On top of this, there should be somebody who manages the DWH. I haven't associated any role to them because one of the core roles can be responsible for more than one step.
In the case of a lakehouse, it is the data engineer who moves the data from the various data sources to the data lake if that doesn't happen already by design or configuration. As per my understanding the data engineers are the ones who design and build the new lakehouse, move transform and manage the data as required. The Data Analysts, Data Scientist and maybe some Information Designers can tap then into the data. However, the DWH and the lakehouse(s) are technologies that facilitate their work. They can still do their work also if the same data are available by other means.
In what concerns the dorm analogy, the verbs were chosen to match the way data warehouses (DWH) or lakehouses are built, though the congruence of the steps is questionable. One could have compared the number of students with the numbers of data entities, but not with the data themselves. Usually, students move by themselves and occupy the places. The story tellers, the assistants and researchers are independent on whether the students are hosted in the dorm or not. Therefore, the analogy seems to be a bit forced.
Frankly, I covered all the steps except the ones related to Data Science by myself for both described scenarios. It helped that I knew the data from the data sources and the transformations rules I had to apply, respectively the techniques needed for moving and transforming the data, and the volume of data entities was manageable somehow. Conversely, 1-2 more resources in the area of data analysis and visualizations could have helped to bring more value to the business.
This opens the challenge of scale and it has do to with systems engineering and how the number of components and the interactions between them increase systems' complexity and the demand for managing the respective components. In the simplest linear models, for each multiplier of a certain number of components of the same type from the organization, the number of resources managing the respective layer matches to some degree the multiplier. E.g. if a data engineer can handle x data entities in a unit of time, then for hand n*x components are more likely at least n data engineers required. However, the output of n components is only a fraction of the n*x given the dependencies existing between components and other constraints.
An optimization problem resumes in finding out what data roles to chose to cover an organization's needs. A one man show can be the best solution for small organizations, though unless there's a good division of labor, bringing a second person will make the throughput slower until will become faster.
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Resources:
[1] Christopher Laubenthal (2024) "Why One Person Can’t Do Everything In Data" (link)
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