Why is horizontal scalability important for MongoDB in analytics?

Horizontal scalability is crucial for MongoDB in analytics because it enables the seamless addition of resources, such as servers or nodes, as data volumes increase. This capability allows organizations to manage expansive datasets more effectively by distributing the load across various machines. As businesses grow and their data accumulates, the ability to add more hardware without significant downtime or complex reconfiguration ensures that performance remains stable and responsive.

In the context of analytics, where large datasets are common, the ability to scale out by adding more nodes can significantly enhance processing power and storage capacity. This effectively addresses demands for rapid growth in data while maintaining optimal performance levels, making MongoDB suitable for applications that require robust analytics functionalities.

The other options do not align with the benefits of horizontal scalability in the context of MongoDB. Limiting the amount of data processed or simplifying data retrieval operations conflicts with the flexibility and performance enhancements that scaling out provides. Similarly, restricting the types of queries runs counter to the advantages of having more resources, which actually allows for more complex and varied analytics capabilities as demands grow.