Apache Kafka - Real-time Stream Processing (Master Class) - Implementing Streams Topology

This video tutorial covers implementing a Kafka Streams topology using a starter project. It includes setting up the project with necessary dependencies, understanding serialization and services, and creating a source stream. The tutorial demonstrates filtering and transforming data streams to meet specific requirements, such as processing home delivery invoices and transforming invoices into notifications. The video concludes with finalizing the stream processing and running the application to observe the output.

What does the starter project include to help you get started with Kafka Streams?

• Just the POJOs needed for the project

• A complete Kafka cluster setup

• Only the Kafka Streams API

• A set of dependencies, logging configuration, and scripts

Why is it important to use specific serializers and deserializers for different data objects in Kafka Streams?

• To ensure data is stored in a database

• To maintain data integrity and format during processing

• To increase the speed of data processing

• To reduce the size of the data

What is the first step in creating a topology in Kafka Streams?

• Configuring the application ID

• Setting up a Kafka producer

• Creating a source stream

• Defining the output topics

How do you filter for home delivery invoices in a Kafka Streams topology?

• By setting a default key value service

• By applying a filter with a predicate

• By using a flat map function

• By using a map function

What transformation is applied to prime customer invoices before streaming them to a Kafka topic?

• They are converted into JSON format

• They are transformed into notification objects

• They are split into multiple records

• They are encrypted for security

Which method is used to transform values in a Kafka Streams pipeline?

• filter

• mapValues

• reduce

• flatMap

What is the purpose of the 'getNotification' method in the transformation process?

• To log errors during processing

• To validate the data format

• To convert invoices into notification objects

• To send notifications to users

What is the final step in the Kafka Streams topology before running the application?

• Adding a shutdown hook

• Creating a new Kafka topic

• Starting the Zookeeper service

• Configuring the bootstrap server

What is the purpose of using a flat map in the final requirement?

• To serialize data into JSON format

• To filter out unwanted data

• To combine multiple streams into one

• To transform invoices into multiple Hadoop records

What should you do if you don't see any output in the Kafka topic after starting the application?

• Reconfigure the application ID

• Check the input topic and start the POS simulator

• Restart the Kafka services

• Increase the logging level