Data management systems have traditionally relied on pull queries, where users run queries to retrieve data from various sources like RDBMS, data lakes, or full-text indexes. This method works well in many cases, but it has notable drawbacks. Performance can falter with large datasets or complex queries, and the data's format, shape, and location can hinder efficient querying. For instance, using a relational database for analytical queries might not yield the best performance. Materialized views can address these challenges by precomputing query results and storing them in an optimized format. This approach enables fast access to complex data relationships, like fetching order details with minimal latency. However, managing data duplication across multiple views raises concerns about consistency. The key is maintaining a single source of truth while allowing derived views to evolve based on the original dataset. Techniques such as tracking timestamps or log sequence numbers can help manage eventual consistency. Push queries present an alternative by reacting to changes in the source data, making them ideal for real-time processing. Instead of recalculating entire datasets, push queries update results incrementally, which can be more efficient. For example, updating revenue totals only for the affected product category when a new order comes in saves processing time. However, push queries may overwhelm users with constant updates, which is where combining push and pull queries becomes valuable. Storing the results of push queries in systems designed for pull queries allows users to access data on demand without dealing with constant streams of updates.