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Home / Blog / Interview Questions on Data Engineering / Top 35 Apache Kafka Interview Questions
Sharat Chandra is the head of analytics at 360DigiTMG as well as one of the founders and directors of AiSPRY. 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.
Table of Content
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zookeeper manages and coordinates the Kafka brokers. It is responsible for leader election, membership, and state management in the Kafka cluster.
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.
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.
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.
The benefits include scalability, reliability, and reduced management overhead. Challenges can include network latency, data transfer costs, and integrating with other cloud services.
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.
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.
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.
Best practices include handling serialization and deserialization of messages, managing consumer offsets, handling exceptions properly, and optimizing producer and consumer configurations.
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.
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.
Exactly-once semantics ensure that each message is processed exactly once, avoiding duplicates. This is achieved through Kafka’s idempotent producers and transactional APIs.
Kafka handles large-scale throughput by distributing data across multiple partitions and brokers, parallel processing, and tuning producer and consumer configurations for optimal performance.
Considerations include the expected message volume and velocity, replication factor, retention requirements, resource usage, and the anticipated growth of data and applications.
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.
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.
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.
Data rebalancing in Kafka, triggered by changes in the cluster, can impact performance. Solutions include minimizing rebalances, using static membership, and optimizing rebalance strategies.
Tuning for low latency involves configuring batch sizes, linger times, compression, and tuning network and I/O settings to balance between throughput and latency.
Strategies include multi-region deployment, replicating data across clusters with MirrorMaker, using ISRs for high data availability, and regular backups.
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.
In event-driven architectures, Kafka acts as the central backbone that decouples data producers from consumers, enabling scalable and flexible microservices communications through events.
Managing Kafka in microservices involves ensuring topic partitioning aligns with service needs, monitoring consumer lag, and isolating resources to prevent noisy neighbors.
Best practices include choosing an appropriate number of partitions, naming topics meaningfully, using compacted topics for configuration-type data, and considering consumer parallelism.
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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