Data Science Model Deployments and Cloud Computing on GCP - Caching and Its Use Cases

The video tutorial explains the importance of caching in data retrieval, especially when dealing with applications deployed on platforms like Google App Engine. It describes how caching can prevent unnecessary data fetching from databases like Bigquery by storing frequently accessed data temporarily. The tutorial covers the workings of caching mechanisms, the types of caching databases such as Redis and Memcache, and the differences between shared and dedicated cache storage in Memcache. It concludes with a brief overview of implementing caching in a Python application.

What is the purpose of caching in data retrieval?

Explain how caching can improve the performance of an application deployed on Google App Engine.

What are the differences between shared cache and dedicated cache?

Describe the process of how an application checks for data in the cache before querying the primary data source.

Why is it important to use caching when the underlying data does not change frequently?