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5 Common Mistakes when developing a Data Platform
How to avoid missteps in building Data Warehouses, Lakehouses or similar Projects
Developing a Data Platform is a complex endeavor that demands meticulous planning and precise execution. Nonetheless, there are several prevalent missteps that can decrease the progress of a data platform initiative. This article will describe five common antipatterns can occur in the building of a data platform.
Mistake 1: Fragmented Data Silos
One of the most prevalent pitfalls in data platform development is the emergence of isolated data silos. This transpires when different factions within a company create separate data solutions that do not work together in harmony. Silos lead to duplicated efforts, incoherent data definitions, and complications in data integration. To avoid this issue, it is crucial to cultivate cross-team cooperation, establish standardized data models, and institute a unified data governance strategy. Also newer technical approaches like the Data Lakehouse and the corresponding Data Mesh architecture will help with better Data sharing processes and the establishment of a data-driven policy within the company.
