We're seeing a worrying proliferation of so-called Reverse ETL. Regular ETL jobs have their place in traditional data architectures, where they transfer data from transaction processing systems to a centralized analytics system, such as a data warehouse or data lake. While this architecture has well-documented shortcomings, many of which are addressed by a data mesh, it remains common in enterprises. In such an architecture, moving data back from a central analytics system to a transaction system makes sense in certain cases — for example, when the central system can aggregate data from multiple sources or as part of a transitional architecture when migrating toward a data mesh. However, we're seeing a growing trend where product vendors use Reverse ETL as an excuse to move increasing amounts of business logic into a centralized platform — their product. This approach exacerbates many of the issues caused by centralized data architectures, and we suggest exercising extreme caution when introducing data flows from a sprawling, central data platform to transaction processing systems.
