Data Science Model Deployments and Cloud Computing on GCP - Lab - Implement Caching Mechanism in Python Application - Pa

The video tutorial explains how to use Flask with memcache and BigQuery. It covers setting up a Flask application, integrating BigQuery to fetch data, and using memcache to cache data for improved performance. The tutorial also touches on the role of Datastore as the underlying database for memcache. The video concludes with a brief overview of the next steps, including an assignment to deploy an application using memcache.

What is the initial step to fetch data from BigQuery in the Flask application?

• Pass an ID as a parameter

• Set a default value in the code

• Open a new tab

• Click on the memcache application

Why is it necessary to convert post IDs to strings in the Python runtime?

• Python runtime only accepts integer data types

• Python runtime only accepts string data types

• To improve application performance

• To ensure data security

What happens when the same value is passed again in the application?

• Data is fetched from BigQuery again

• Data is fetched from memcache

• Data is stored in a new cache

• Data is deleted from the cache

What is the role of 'kind' in Datastore?

• It defines the type of entity stored

• It sets the data retrieval speed

• It determines the cache duration

• It specifies the database size

What is the underlying database used by memcache?

• MySQL

• PostgreSQL

• Datastore

• MongoDB