Apache Kafka is one of the most popular distributed streaming platforms used for building real-time data pipelines and event-driven applications. Whether you're interviewing for a backend developer, data engineer, or DevOps role, Kafka questions are often a key part of the process.
Here’s a collection of 20 essential Kafka interview questions and answers to help you nail your next technical interview.
1. What is Apache Kafka?
Apache Kafka is a distributed streaming platform used to build real-time data pipelines and applications. It can publish, subscribe, store, and process streams of records in a fault-tolerant way and is known for high throughput and low latency.
2. Explain the main components of Kafka.
- Producer: Sends data to Kafka topics.
- Consumer: Reads data from topics.
- Broker: A Kafka server that stores and manages data.
- Topic: A category to which records are published.
- Partition: A topic can have multiple partitions for parallelism.
- Offset: Unique ID of a message in a partition.
- Consumer Group: A set of consumers working together.
3. How does Kafka ensure data durability and fault tolerance?
Kafka uses replication. Each partition has one leader and multiple follower replicas. If the leader fails, a follower is elected as the new leader. This replication ensures no data loss and high availability.
4. What is a Kafka topic, and how is it partitioned?
A topic is a named stream of records. Topics are split into partitions, which allow for parallelism. Each partition is an ordered sequence of messages, each with a unique offset.
5. How do producers send data to specific partitions?
- Round-robin (default) for even distribution.
- Key-based partitioning using hash(key).
- Custom partitioner with user-defined logic.
6. What is the role of the Kafka broker?
A Kafka broker:
- Stores records in partitions.
- Serves producer and consumer requests.
- Manages metadata and replication.
- Handles offset tracking.
7. What are consumer groups in Kafka?
A consumer group allows multiple consumers to coordinate consumption. Each partition is assigned to only one consumer within a group, enabling scalable and fault-tolerant message processing.
8. How does Kafka handle message ordering?
Kafka guarantees ordering only within a partition. Messages are written and read in the same order. No ordering is guaranteed across partitions.
9. What is Kafka’s log compaction feature?
Log compaction keeps only the latest value for each key in a topic, deleting older versions. It’s useful for maintaining state or snapshots, such as change-data-capture (CDC) or recovery scenarios.
10. What are the different types of Kafka clients?
- Producer API: Send messages.
- Consumer API: Read messages.
- Streams API: Process real-time data.
- Connect API: Integrate with other systems (DBs, file systems).
11. How does Kafka achieve high throughput?
- Batching: Groups messages into single requests.
- Asynchronous I/O: Non-blocking operations.
- Zero-copy: Efficient data transfer.
- Partitioning: Parallelism across topics and consumers.
12. What is the role of Zookeeper in Kafka?
Zookeeper manages:
- Cluster metadata and broker registration.
- Leader election for partitions.
- Health tracking of brokers.
- ACL and configuration management.
13. Kafka vs. Traditional Messaging Systems?
FeatureKafkaTraditional MQ (e.g., RabbitMQ)RetentionConfigurableDeletes after consumptionScalabilityHighly scalableLimited scalabilityThroughputVery highModerateProcessingStream supportTask queues
14. What is the Kafka Streams API?
Kafka Streams is a Java library for building real-time applications. It supports:
- Stateful/stateless transformations
- Joins and windowing
- Fault-tolerant state stores
15. How do you monitor Kafka?
- JMX metrics
- Kafka Manager
- Prometheus + Grafana
- Burrow for consumer lag
- Elastic Stack (ELK) for log analysis
16. What is Kafka Connect and its use cases?
Kafka Connect is used for data integration with external systems. Use cases:
- ETL pipelines
- Database CDC
- Cloud data migration
- Search/index sync
17. Kafka vs RabbitMQ
CriteriaKafkaRabbitMQModelDistributed logMessage queueRetentionConfigurableDeletes after deliveryThroughputHighModerateUse CaseStreaming, analyticsRPC, task queues
18. How does Kafka ensure data consistency?
- Replication across brokers
- Leader-based reads/writes
- Acknowledgment levels (acks=all) to confirm data is stored in all replicas
19. How does Kafka handle backpressure?
- Consumer lag monitoring
- Flow control from producer
- Scaling consumers and partitions
- Retries and error handling at application layer
20. Best Practices for Kafka Deployment
- Set appropriate replication factor
- Monitor disk I/O, network, CPU
- Secure Kafka with SASL, SSL, ACLs
- Use topic-level retention policies
- Manage consumer offsets properly
- Scale brokers and partitions as needed
🧠 Final Thoughts
Mastering Kafka fundamentals — from topics and partitions to producers, consumers, and internals — is essential for backend engineers and data professionals. These 20 questions cover the most important areas you'll encounter in interviews.