Case Study – MovieFlix

The video tutorial explains how to implement a video streaming service using Kafka. It covers setting up Kafka for tracking video positions, implementing a resume service, creating real-time recommendations, and using analytics for data storage. The tutorial also discusses configuring Kafka topics for optimal performance, focusing on user data ordering and partitioning strategies.

What is one of the main capabilities that Movie Flicks wants to implement?

• Downloading movies for free

• Live streaming of events

• Resuming videos where left off

• Offline viewing of shows

In the Kafka architecture, what role does the video position service play?

• It is a proxy that produces data to Kafka

• It stores user profiles in a database

• It directly recommends shows to users

• It acts as a consumer of the show position topic

How does the recommendation engine determine user preferences?

• By conducting user surveys

• By tracking how far users watch shows

• By monitoring social media activity

• By analyzing user comments

What is the purpose of the analytics consumer in the Kafka architecture?

• To handle video playback issues

• To manage user subscriptions

• To directly recommend shows to users

• To store data in an analytics store for further processing

Why is user ID chosen as a key for the show position topic?

• To increase data redundancy

• To reduce the number of partitions

• To maintain order of data for individual users

• To ensure data is ordered across all users

What is a characteristic of the recommendations topic in Kafka?

• It has a high volume of data

• It uses multiple keys for partitioning

• It requires frequent updates every 30 seconds

• It is a low volume topic

What is a key consideration when determining the number of partitions for the show position topic?

• The number of users watching shows

• The type of shows being watched

• The geographical location of users

• The frequency of data updates