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Top 35 Apache Kafka Interview Questions

  • November 20, 2023
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Meet the Author : Mr. Sharat Chandra

Sharat Chandra is the head of analytics at 360DigiTMG as well as one of the founders and directors of Innodatatics Private Limited. With more than 17 years of work experience in the IT sector and Worked as a Data scientist for 14+ years across several industry domains, Sharat Chandra has a wide range of expertise in areas like retail, manufacturing, medical care, etc. With over ten years of expertise as the head trainer at 360DigiTMG, Sharat Chandra has been assisting his pupils in making the move to the IT industry simple. Along with the Oncology team, he made a contribution to the field of LSHC, especially to the field of cancer therapy, which was published in the British magazine of Cancer research magazine.

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Table of Content

  • What is Apache Kafka, and how is it used in data engineering?

    Real-time data pipelines & streaming applications may be constructed with Apache Kafka, a distributed streaming platform. Stream processing and fault-tolerant, high-throughput communications are two applications for it.

  • How does Kafka's pub-sub messaging model work?

    In Kafka's pub-sub model, producers publish messages to topics, from which consumers then subscribe and consume messages. This decouples the production of data from its processing.

  • What are Kafka Brokers, and what role do they play?

    Kafka brokers are servers that store data and serve clients. They form a Kafka cluster, handling requests from producers to store messages and serving messages to consumers.

  • Explain Kafka Connect and its use cases.

    Kafka Connect is a tool for streaming data between Apache Kafka and other systems, such as databases, in a scalable and reliable way. It's used for integrating Kafka with external data sources and sinks.

  • What is the Kafka Schema Registry, and why is it important?

    Kafka Schema Registry stores and retrieves Avro schemas for Kafka messages. It ensures that the data format is consistent and compatible across the Kafka ecosystem.

  • What are Kafka topics, and how are they structured?

    Kafka topics are categories or feed names where messages are stored and published. Topics are divided into partitions, which allow Kafka to parallelize processing by distributing the data across the Kafka cluster.

  • How do partitions in Kafka topics affect scalability and performance?

    Partitions allow Kafka to scale horizontally by distributing data across multiple brokers. More partitions lead to higher parallelism and throughput, but also more overhead in managing them.

  • What is the significance of the partition key in Kafka?

    The partition key determines which partition a particular message will be sent to. It ensures that messages with the same key (like the same user ID) always go to the same partition, preserving order within that key.

  • What is Kafka Streams, and what are its capabilities?

    Kafka Streams is a client library for building applications and microservices where the input and output data are stored in Kafka topics. It provides stateful processing, windowing, and exactly-once processing semantics.

  • How does Kafka handle real-time data streaming?

    Kafka handles real-time data streaming by processing and passing messages with very low latency. It allows for real-time analytics and data processing directly from the stream of data.

  • What is the role of Zookeeper in Kafka?

    Zookeeper manages and coordinates the Kafka brokers. It is responsible for leader election, membership, and state management in the Kafka cluster.

  • How do Kafka producers and consumers work?

    Kafka producers create and send messages to topics. Consumers read messages from topics. They can join consumer groups to balance load and ensure each message is processed by only one consumer in the group.

  • What is consumer group in Kafka, and how does it ensure scalability?

    A consumer group is a set of consumers which jointly consume messages from a topic. Each consumer in the group reads from exclusive partitions of the topic, enabling scalable parallel processing.

  • How is Kafka used in cloud environments like AWS, GCP, and Azure?

    Kafka can be deployed on cloud platforms using managed services like Amazon MSK (Managed Streaming for Kafka), Azure Event Hubs for Kafka, and Google Cloud's Pub/Sub. These services provide Kafka’s capabilities while offloading the operational overhead.

  • Explain the benefits and challenges of running Kafka on cloud platforms?.

    The benefits include scalability, reliability, and reduced management overhead. Challenges can include network latency, data transfer costs, and integrating with other cloud services.

  • What are ISR brokers in Kafka?

    ISR (In-Sync Replicas) are brokers in a Kafka cluster that have up-to-date copies of the partitions. They ensure high availability and durability of messages.

  • How does Kafka ensure message durability and fault tolerance?

    Kafka ensures durability by persisting messages on disk and replicating them across multiple brokers. Fault tolerance is achieved through ISRs and automatic leader election in case of broker failure.

  • How can Kafka be integrated with Python applications?

    Python applications may create and receive messages from Kafka topics by integrating Kafka with them using libraries such as confluent-kafka-python or kafka-python.

  • What are some best practices for using Kafka with Python?

    Best practices include handling serialization and deserialization of messages, managing consumer offsets, handling exceptions properly, and optimizing producer and consumer configurations.

  • What are Kafka's log compaction and retention policies?

    Log compaction in Kafka ensures that the log contains at least the last known value for each key. Retention policies determine how long messages are kept in topics before being deleted.

  • How do you monitor and manage Kafka performance?

    Kafka performance is monitored and managed using tools like Kafka’s JMX metrics, Consumer Lag monitoring, and third-party tools like Datadog, Prometheus, or Grafana.

  • What is exactly-once semantics in Kafka, and how is it achieved?

    Exactly-once semantics ensure that each message is processed exactly once, avoiding duplicates. This is achieved through Kafka’s idempotent producers and transactional APIs.

  • How does Kafka handle large-scale message throughput?

    Kafka handles large-scale throughput by distributing data across multiple partitions and brokers, parallel processing, and tuning producer and consumer configurations for optimal performance.

  • What are the considerations for Kafka’s cluster sizing and scalability?

    Considerations include the expected message volume and velocity, replication factor, retention requirements, resource usage, and the anticipated growth of data and applications.

  • How do you handle data security and encryption in Kafka?

    Data security in Kafka is handled through SSL/TLS encryption for data in transit, SASL for authentication, ACLs for authorization, and encrypting data at rest at the storage level.

  • What are Kafka MirrorMaker and its use cases?

    Kafka MirrorMaker is a tool used for cross-cluster data replication. It's used for disaster recovery, aggregating data from multiple clusters, and geo-replication.

  • How do you handle schema evolution and compatibility in Kafka?

    Schema evolution is managed using the Schema Registry, which stores schema versions. Compatibility is ensured through schema compatibility checks, allowing schemas to evolve without breaking applications.

  • Discuss the challenges and solutions of Kafka data rebalancing.

    Data rebalancing in Kafka, triggered by changes in the cluster, can impact performance. Solutions include minimizing rebalances, using static membership, and optimizing rebalance strategies.

  • How do you tune Kafka for low-latency message delivery?

    Tuning for low latency involves configuring batch sizes, linger times, compression, and tuning network and I/O settings to balance between throughput and latency.

  • What are the strategies for disaster recovery and high availability in Kafka?

    Strategies include multi-region deployment, replicating data across clusters with MirrorMaker, using ISRs for high data availability, and regular backups.

  • How does Kafka integrate with stream processing frameworks like Spark and Flink?

    Kafka integrates with stream processing frameworks as a source and sink of real-time data streams. Spark and Flink provide connectors to consume data from Kafka, process it, and optionally write back to Kafka.

  • Explain the role of Kafka in event-driven architectures.

    In event-driven architectures, Kafka acts as the central backbone that decouples data producers from consumers, enabling scalable and flexible microservices communications through events.

  • How do you manage Kafka in a microservices environment?

    Managing Kafka in microservices involves ensuring topic partitioning aligns with service needs, monitoring consumer lag, and isolating resources to prevent noisy neighbors.

  • What are the best practices for Kafka topic and partition design?

    Best practices include choosing an appropriate number of partitions, naming topics meaningfully, using compacted topics for configuration-type data, and considering consumer parallelism.

  • Discuss the future trends and evolution in Kafka and streaming platforms.

    More emphasis on real-time analytics, improved stream processing capabilities, closer cloud integrations, and the rising significance of data governance for streaming platforms are some of the next developments.


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