The video tutorial covers key aspects of real-time stream processing using Kafka. It begins with an introduction to three standard approaches and a simple produce-transform-consume pipeline. The complexities of using Kafka's producer and consumer APIs are discussed, with resources provided for further learning. The focus then shifts to Kafka Streams, exploring API structure and topology modeling. The tutorial concludes with strategies for scaling Kafka Streams applications, emphasizing vertical and horizontal scaling techniques.

What are the three standard approaches discussed for implementing real-time stream processing in Kafka?

• Producer-Consumer, Batch Processing, Real-time Analytics

• Producer-Consumer, Stream Processing, Batch Processing

• Stream Processing, Real-time Analytics, Data Warehousing

• Batch Processing, Data Warehousing, Real-time Analytics

What is a key challenge when using the Kafka Consumer API for real-time stream processing?

• Limited support for multiple languages

• Complexity and challenges in real-life problem solving

• Lack of scalability

• Inability to handle large data volumes

What is the primary focus when dealing with real-time stream processing requirements in Kafka?

• Kafka Consumer API

• Kafka Streams

• Batch Processing

• Data Warehousing

What should you refer to for understanding the structure of Kafka Streams API?

• Kafka Consumer Java documentation

• KStream package javadoc

• Kafka Producer API guide

• YouTube tutorials on Kafka

How can you achieve horizontal scaling in Kafka Streams applications?

• By optimizing the code

• By starting new instances on different machines

• By increasing the number of threads

• By using a single-threaded model