Apache Kafka is an open-source stream-processing software platform developed by Linkedin and donated to the Apache Software Foundation. It is designed to handle data streams and real-time analytics.


  1. Scalability: Kafka can scale horizontally, allowing for more volume.
  2. Durability: Data is replicated across multiple brokers to ensure message durability. This relates to the question “How does Kafka ensure message durability?”
  3. Fault Tolerance: Kafka is built to recover from broker failures, designating a new leader for partitions that were managed by a failed broker. This is covered in the question “How does Kafka handle failure in brokers?”
  4. Real-Time: Kafka supports low-latency delivery, making it ideal for real-time analytics and monitoring.

The Basic Components of Apache Kafka

  1. Producer: Pushes messages to Kafka topics. Producers determine which partition to send a message to, either using round-robin or a partitioning key.
  2. Consumer: Reads messages from topics. Consumers are often organized into consumer groups for parallel consumption of data.
  3. Broker: Kafka servers that store data and serve clients. Each broker has a unique ID, known as the broker ID.
  4. Topic: Categories where messages are stored. A topic can be divided into multiple partitions for parallelism.
  5. Zookeeper: Manages the distributed nature of Kafka. Zookeeper is crucial for broker coordination but newer versions of Kafka aim to eliminate the dependency on Zookeeper (known as KRaft mode).
  6. Partition: Kafka topics are split into partitions for more parallelism and higher throughput. Data ordering is maintained within each partition.

How Kafka Differs from Other Messaging Systems

  1. Durability: Kafka is more durable due to its distributed architecture.
  2. Throughput: Kafka is designed to handle more messages per second.
  3. Flexibility: Kafka can be used for stream processing, real-time analytics, and data lakes.
  4. Schema: Kafka supports message schemas through a Schema Registry.

The Role of Apache Kafka in Data Streaming

Kafka serves as the backbone for real-time analytics and monitoring. It is used for:

  1. Stream Processing: Manipulating data streams in real-time. Kafka Streams can utilize local state stores for stateful operations.
  2. Event Sourcing: Capturing changes to application state as a series of events.
  3. Decoupling: Kafka decouples data pipelines, allowing independent scaling and failure recovery.

Use Cases: Where Apache Kafka Excels

  1. Real-Time Analytics
  2. Data Lakes
  3. Aggregating Data from Different Sources
  4. Monitoring
  5. ETL Pipelines

Questions for CCKAD on Apache Kafka

  1. What is the role of Zookeeper in Kafka?
  2. How does Kafka ensure message durability?
  3. Explain the concept of partitions in Kafka.
  4. What are consumer groups in Kafka?
  5. How does Kafka handle failure in brokers?
  6. What is a Kafka topic and how is it different from a queue?
  7. What are the benefits of having multiple partitions in a Kafka topic?
  8. How does a Kafka producer know which partition to send a message to?
  9. Explain the significance of a Kafka broker ID.
  10. How can you secure Kafka?
  11. How does Kafka ensure data ordering?
  12. What is the role of the Schema Registry in Kafka?
  13. What is the difference between a Kafka Stream and a Kafka Table?
  14. What is meant by “log compaction” in Kafka?
  15. What are Kafka Connectors?
  16. What is idempotent writing in Kafka?
  17. How can you ensure exactly-once message processing in Kafka?
  18. How does Kafka support data retention?
  19. What is a Kafka MirrorMaker?
  20. Can Kafka be used without Zookeeper? Explain.
  21. What is “linger time” in Kafka?
  22. What is the significance of the acks setting in a Kafka producer?
  23. What is the role of a Controller in a Kafka cluster?
  24. What are state stores in Kafka Streams?
  25. How does Kafka support message replayability?

Solutions Questions

  1. Zookeeper manages the distributed nature of Kafka and handles broker coordination.
  2. Durability is ensured by replicating messages across multiple brokers.
  3. Partitions allow for horizontal scalability and parallelism.
  4. Consumer Groups are consumers organized into groups for parallel consumption of data.
  5. Failure Handling is managed by designating a new leader for partitions managed by a failed broker.
  6. Kafka Topic is more flexible than a queue and allows for multiple consumers to read from it concurrently.
  7. Multiple Partitions provide higher throughput and scalability.
  8. Producer either uses round-robin or a partitioning key to determine the target partition.
  9. Broker ID is a unique identifier for each broker in a Kafka cluster.
  10. Security can be ensured through SSL, SASL, and ACLs.
  11. Data Ordering: Kafka maintains the order of messages within each partition.
  12. Schema Registry: Stores and retrieves message schemas, allowing for backward or forward compatibility.
  13. Kafka Stream vs Table: A stream is an immutable sequence of data records, while a table is a mutable state, representing latest values.
  14. Log Compaction: Old records are removed, keeping only the latest record for each unique key within a partition.
  15. Kafka Connectors: They enable integration with databases, key-value stores, and other systems.
  16. Idempotent Writing: Ensures that records are written exactly once to the destination, avoiding duplicates.
  17. Exactly-Once Semantics: Achieved by combining idempotent producers and transactional guarantees.
  18. Data Retention: Configurable time or size-based policies for retaining data.
  19. MirrorMaker: Tool for replicating data between two Kafka clusters.
  20. Without Zookeeper: Newer Kafka versions aim for KRaft mode, where Zookeeper is not required, but currently, Zookeeper is integral.
  21. Linger Time: Time a producer waits, aiming to batch records.
  22. acks Setting: Controls the number of broker acknowledgements for message writes.
  23. Controller: A designated broker responsible for administrative tasks like assigning partitions to brokers.
  24. State Stores: Local storage attached to a Kafka Stream processor, allowing for stateful operations.
  25. Message Replayability: Old messages are stored for a configurable amount of time, allowing for replay.