Case Study – GetTaxi

The video tutorial discusses the Get Taxi company, which connects users with taxi drivers on demand. It outlines the business requirements, such as matching users with nearby drivers and implementing surge pricing. The tutorial explains how to use Kafka for data processing, including user and taxi position topics, and proposes an architecture for handling high-volume data. It also covers the development of a search pricing model using Kafka streams and the importance of data analytics, suggesting Amazon S3 for storage. The tutorial emphasizes the need for efficient data handling and retention strategies.

What is one of the primary business requirements for the Get Taxi system?

• To store user payment information

• To offer discounts during peak hours

• To match users with nearby drivers

• To provide free rides to users

In the proposed architecture, what role does the user solution service play?

• It calculates the cost of rides

• It stores user payment information

• It acts as a proxy to receive data from user applications

• It directly connects mobile phones to Kafka

Why are user and taxi positions stored in separate Kafka topics?

• Because they are different entities with high data volume

• To simplify the architecture

• To reduce data volume

• Because they are considered the same entity

What additional data sources can be integrated into the Kafka streams application for better accuracy?

• User payment history

• User social media profiles

• Weather and events data

• Driver feedback

What is the purpose of using Kafka streams in the surge pricing model?

• To perform computations on multiple input topics

• To store user data permanently

• To connect directly to user applications

• To reduce the number of Kafka topics