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The feature that supports modular development in dbt is the use of packages and model references. In dbt, modularity is achieved through the creation of reusable components, which allows for cleaner, more maintainable code. By using packages, analysts can easily incorporate pre-built models and analyses that are shared within the dbt community. This fosters collaboration and accelerates development, as teams can leverage existing resources rather than building everything from scratch.

Model references are another key aspect of modular development. They allow one model to reference another, which promotes logical structuring of transformations and dependencies within the data pipeline. This referencing ensures that changes propagate correctly and that models can be built upon one another in a coherent and systematic way.

While database indexing, automated data cleansing, and real-time data processing are important features in data management and analysis, they do not specifically address the modular development aspect that dbt emphasizes. Modularity is all about organizing and reusing code effectively, which is primarily facilitated through packages and model references in the dbt ecosystem.