Identify the principles that you need to follow during modeling.

In the context of modeling within SAP HANA, the principle of reducing data transfer between views by applying filters as low down as possible is essential for optimizing performance. This approach allows for the minimization of the volume of data that needs to be transferred between different components of the system, which is crucial considering the speed and efficiency goals within the HANA environment.

When filters are applied early in the data processing pipeline—ideally at the data source level—it ensures that only the necessary data is brought into memory for further processing. This not only enhances the response time for queries but also makes more efficient use of system resources, as less data means reduced memory usage and lower load on the network.

In contrast, applying filters higher up in the data processing sequence can result in significant inefficiencies, as larger datasets are transferred before filtering. This increases latency and can lead to a bottleneck in performance, which is contrary to the design principles of the HANA architecture that aims for high throughput and low latency.

By emphasizing this principle, one can ensure that the HANA system performs optimally, leveraging its in-memory capabilities more effectively and adhering closely to best practices in data modeling.