Kafka has grown in leaps and bounds over the past years and has been adopted by more than 30% of the Fortune 500 companies for their big data operations including banks, insurance, travel, and telecom companies among others. They use Apache Kafka for ingestion and real-time processing of large streams of data thanks to its excellent performance. Other uses include web activity monitoring, metrics collection, log aggregation, and distributed commit log for in-memory microservices. Kafka offers several advantages including high throughput, durability, scalability, reliability, and replication factor which makes it ideal for a range of tasks. 

Given the growing popularity of Kafka as a powerful messaging technology, the demand for Apache Kafka skills in the data science, analytics, and software engineering industry has spiked. Kafka certification is crucial for any professional hoping to further their career prospects in the big data space. 

Common Kafka interview questions

After earning the Kafka certification, you need to make it past the Kafka interview by not only impressing the panel but also demonstrating your practical knowledge and skills in Kafka. Here are 15 common Kafka technology interview questions with their answers that will help you ace the interview. 

1. What is Kafka? Highlight its core features. 

Apache Kafka is an open-source data processing technology developed by Apache Software Foundation that is widely used for ingesting and moving large volumes of data fast. It is written in Scala and Java languages and designed to provide high throughput and low-latency for real-time streaming of large volumes of messages.

Kafka has the following core features:

• It has high throughput to deliver a constant level of performance with modest hardware even when brokering millions of messages streaming at a high velocity. 
• It is highly scalable and can be scaled quickly and easily with no downtime since it is a distributed system. 
• It is a replication system that replicates messages across the cluster of servers to ensure durability and high availability of successfully published messages without the risk of loss in the event of a server failure. 
• It is highly durable. Replication of messages on the disks across the clusters supports the persistence of messages on the disk. 

2. What are some of the common use cases of Apache Kafka? 

Some common use cases of Kafka include; 

• Stream processing 
• Web activity monitoring
• Metrics collection and monitoring 
• Messaging 
• Distributed commit logging
• Log aggregation 

3. What are the components of Kafka? 

The core components of Kafka are: 

• Topic – A collection of messages that belong to the same type.
• Producer – Kafka function that issues communication and publishes messages to a topic. 
• Broker – A server that stores published messages.
• Consumer – Kafka’s consumer function subscribes to topics and also reads and processes messages from the topics. 

What is the role of the ZooKeeper in a cluster in Kafka? Can we use Kafka without the ZooKeeper? 

ZooKeeper is an open-source, distributed configuration and synchronization function that issues communication between cluster nodes and to commit offset such that in the event that a node fails it can recover from the previously committed offset. It also performs other tasks like communicating with Kafka when a new broker joins or dies as well as keeping track of Kafka topics, partitions, and node status in real-time. 

Kafka is designed to use ZooKeeper thus cannot operate without it. 

1. What is the role of the offset? 

The offset in Kafka is a unique sequential ID number allocated to messages in the partition. With the help of the ZooKeeper, Kafka partitions, and stores the offsets of messages based on the specific topic consumed by a consumer group. 

2. What is the process of starting a Kafka server?

The Kafka server cannot operate without the ZooKeeper application. 

Therefore, the first step is initializing ZooKeeper by executing the below command:

bin/zookeeper-server-start.sh config/zookeeper.properties

Next, start the Kafka server by initiating the following command

bin/kafka-server-start.sh config/server.properties

3. List the main Kafka APIs 

Apache Kafka has five core APIs. These are: 

• Producer API which allows applications to send streams of data to topics in Kafka cluster nodes. 
• Consumer API allows applications to subscribe to topics and process output records produced to them.   
• Streams API which transforms input topics to output topics. 
• Connector API implements connectors that run and build reusable producers or consumers that connect Kafka topics to existing applications. 
• AdminClient API which manages and inspects topics, brokers, and other Kafka objects. 

What is consumer lag and how is it monitored? 

Consumer lag happens when there is a delay between the time the broker publishes a message and the consumer reads the message. 

There are several Kafka technologies used to monitor consumer lag such as LinkedIn’s Burrow. 

When does the QueueFullException emerge inside the manufacturer?

When the producer tries to send messages at a speed that overwhelms the Broker, QueueFullException happens. The way around this is for users to add brokers to handle the increased workload collaboratively since the producer is not designed to block extra workload. 

What is the difference between Kafka and Flume? 

Apache Kafka is an open-source distributed data system that has been designed to work as a pull system for ingesting and processing data feeds in real-time. It is mainly used as a public-subscribe message system and is suitable in situations that need highly reliable, scalable systems that have the capacity to connect multiple systems and not only Hadoop. However, reading and writing data into Hadoop is not as easy with Kafka as it is with Flume. 

Apache Flume, also a distributed data system designed for aggregating, collecting, and moving massive volumes of streaming data from multiple sources into a central data store. As such, it works as a pull system. It is specially designed to integrate with Hadoop and has been written in Java language. Flume features a simple design with sinks, resources, and channels for moving data. While it is reliable, it is neither scalable nor fault-tolerant therefore, in case of an agent failure, you will lose events on the channel. Apache Flume is a good option when working with non-relational databases.

Explain the concept of Leader and Follower in Kafka. 

Each partition in Kafka has one server that takes the Leader role and none or more servers that act as followers. The leader performs all read and write requests in a partition while Followers replicate the leader passively. In the event that the Leader fails, one follower takes the Leader’s role to ensure load balancing.  

Why are replications important in Kafka? 

Replications ensure that all published messages are highly available and that none gets lost in the process of a system error or software upgrade. 

What does it mean when a preferred replica is not in the ISR? 

When a preferred replica is not in the ISR it means that the controller will be unable to move leadership to the preferred replica. 

What is the role of a partition key within the Producer? 

The role of the partitioning key within the producer is to determine the destination partition to which a message is committed. 

What is the maximum size of a message that can be received by Kafka?  

The maximum size of a message that can be received by Kafka is approximately 100,000 bytes. 

Conclusion 

The Kafka certification training is designed to equip professionals with the knowledge and skill required for big data developers and analysts. Kafka is a powerful stream messaging platform that has been adopted widely by big names like Netflix, Spotify, Uber, and Pinterest. Its adoption over five years has increased by up to 300% and is still rising. Apache Kafka skills are in high demand and the average annual salary of a software engineer according to Payscale is $87,500.