How to do data centralization the right way

Data centralization has historically been the default approach in big data projects, but it has faced challenges due to its monolithic nature, leading to inefficiencies and backlogs in data engineering teams. In contrast, data decentralization gained popularity as a way to alleviate these issues, although it also poses difficulties when implemented without the right tools. The article advocates for a balanced approach, termed "Centralization 2.0," which allows organizations to choose between centralization and decentralization on a case-by-case basis, leveraging modern data platforms like data lakehouses. These platforms enable a flexible architecture that supports both centralized and decentralized data scenarios, enhancing performance, governance, and analytics capabilities. By adopting an evolutionary approach to centralization, data teams can undertake smaller, iterative projects, thereby optimizing data management and ensuring the strategy aligns with business needs. Tools like Starburst's Open Hybrid Data Lakehouse offer a framework to manage data effectively, allowing organizations to mix and match centralized and decentralized workloads to achieve optimal performance and cost efficiency.