Top 25 Kafka Interview Questions & Answers

1. Introduction

Navigating the realm of event streaming platforms, "kafka interview questions" are a crucial gauge for assessing expertise in managing high-throughput data systems. This article serves as a repository of key questions that probe into the understanding of Apache Kafka—a tool that has become synonymous with real-time data processing and distributed systems.

Kafka Interview Insights

Kafka has emerged as a pivotal platform in the landscape of real-time data streaming and processing. Originating from LinkedIn and later becoming part of the Apache Software Foundation, Kafka is lauded for its high throughput, scalability, and fault-tolerance, which are essential in modern data-driven applications. Professionals seeking roles involving Kafka need a solid grasp of its architecture, core principles, and practical applications. They must also be adept at troubleshooting, optimization, and ensuring data integrity. This section delves into the nuances of Kafka’s ecosystem and the proficiencies expected from candidates in roles related to this powerful event streaming platform.

3. Kafka Interview Questions

Q1. Can you explain what Apache Kafka is and why it is used? (Conceptual Understanding)

Apache Kafka is a distributed event streaming platform that is designed to handle high volumes of data and enables the building of real-time streaming data pipelines and applications. Originally developed at LinkedIn and later open-sourced as part of the Apache Software Foundation, Kafka is widely adopted for various use cases including real-time analytics, data integration, and mission-critical application events.

Kafka is used because it offers:

• High throughput: Kafka can handle a high volume of messages without significant performance impact.
• Scalability: It can be scaled out by adding more brokers to the cluster to accommodate more partitions and hence a higher load.
• Durability and Reliability: Kafka replicates data and is able to withstand node failures within a cluster.
• Fault Tolerance: With its replication feature, Kafka ensures messages are not lost and can be recovered even in cases of node failures.
• Low Latency: Kafka is designed to have low latency message delivery, which is crucial for many real-time applications.

Q2. Why do you want to work with Kafka? (Motivation & Fit)

How to Answer

This question seeks to understand your personal motivation and how you see Kafka fitting into your career path. You should focus on your interest in data streaming, real-time analytics, distributed systems, or the technical challenges that Kafka addresses and how these align with your career goals.

Example Answer

I want to work with Kafka because I have a strong interest in real-time data processing and distributed system architectures. Kafka is at the heart of many modern data-driven applications, and it presents exciting technical challenges when it comes to scalability, reliability, and performance. Working with Kafka will allow me to contribute to critical infrastructures and develop my expertise in an area that is becoming increasingly important in today’s data-centric world.

Q3. How does Kafka’s publish-subscribe model work? (Core Concepts)

In Kafka’s publish-subscribe model, producers publish messages to topics from which consumers can subscribe and consume the messages. This model works as follows:

• Producers are applications or systems that send (publish) messages to a Kafka topic.
• Topics are named feeds or categories to which messages are published. Topics act as a channel for message flow.
• Brokers are Kafka servers that store data and serve clients.
• Consumers are applications or systems that read (subscribe to) messages from topics.

Messages within a topic are retained for a configurable period and are not deleted after consumption, meaning multiple consumers can read the same message at different times.

Q4. What is a Kafka Topic and how is it different from a Partition? (Kafka Architecture)

A Kafka topic is a category or feed name to which records are published. Topics in Kafka are multi-subscriber; that is, they can have zero, one, or many consumers that subscribe to the data written to it.

On the other hand, a partition is a division within a topic. Each topic can be split into multiple partitions, where each partition is an ordered, immutable sequence of records. Partitions are distributed across the brokers in the Kafka cluster, with each broker handling data and requests for a share of the partitions.

Aspect	Kafka Topic	Kafka Partition
Definition	A category or feed name to which messages are published.	A segment or a split within a topic that holds a subset of the topic’s data.
Ordering	Does not guarantee ordering of messages across the topic.	Guarantees ordering of messages within the partition.
Distribution	The topic is a logical concept and doesn’t dictate data distribution.	Partitions are distributed across Kafka brokers for scalability and fault tolerance.
Scalability	Topics facilitate message categorization and are not directly related to scalability.	Partitions directly enable scalability as more partitions mean more parallelism.
Consumer Read	Multiple consumers can read from a topic concurrently.	Multiple consumers from the same consumer group will read from exclusive partitions to balance the load.

Q5. Can you describe the role of a Kafka Producer? (Component Functionality)

A Kafka Producer is a component or application that is responsible for publishing records or messages to Kafka Topics. The role of a Kafka Producer includes the following:

• Sending messages: Producers create and send messages to a specific Kafka topic.
• Partition selection: The producer can specify a partition within a topic to send messages to, or it can rely on Kafka’s default partitioning strategy, which might be based on round-robin or a key-based approach.
• Serialization: Producers are responsible for serializing message keys and values to byte arrays before sending them over the network.
• Error handling: Producers need to handle errors that may occur during the message sending process, such as retriable or fatal errors.
• Acknowledgment: Producers can wait for acknowledgments from brokers to ensure that messages have been committed to the Kafka log.

Producers play a crucial role in Kafka’s architecture by acting as the entry point for data into the Kafka cluster. Their efficient operation is key to ensuring high throughput and low-latency data ingestion into Kafka topics.

Q6. How does a Kafka Consumer work, and what is a Consumer Group? (Component Functionality)

A Kafka Consumer is a client or application that reads data from Kafka topics. It subscribes to one or more topics and processes the stream of records produced to those topics. Here’s how it works:

• Offsets: Each consumer keeps track of the offset, which is a pointer to the last record it has consumed. Offsets are committed to Kafka, which allows consumers to pick up where they left off in the event of a restart or failure.
• Fetching: Consumers pull data from Kafka by making periodic fetch requests to the Kafka brokers that hold the data.
• Consumer Groups: Consumers are typically organized into consumer groups for scalability and fault tolerance. Each consumer within a group reads from exclusive partitions of the topics they subscribe to, ensuring that each record is delivered to one consumer in the group, and effectively load balancing the data processing.

A Consumer Group is a set of consumers sharing a common group identifier. Kafka guarantees that each partition is only consumed by one consumer in the group, and as consumers are added or removed, Kafka will rebalance the partitions between them.

Q7. What are some of the key features of Kafka? (Feature Knowledge)

Some of the key features of Kafka include:

• High Throughput: Kafka is designed to handle high volumes of data and to enable the processing of these messages at high speeds.
• Scalability: Kafka can scale out without downtime. New nodes can be added, and Kafka will rebalance itself automatically.
• Durability and Reliability: Kafka uses a distributed commit log, which means messages are persisted on disk and replicated within the cluster to prevent data loss.
• Fault Tolerance: Kafka is resilient to node failures within a cluster.
• Real-time: Kafka is capable of handling real-time data feeds with minimal delay.

Here is a markdown table summarizing some of Kafka’s features:

Feature	Description
High Throughput	Handles high volumes of data efficiently.
Scalability	Easily scales out to accommodate more data or processing.
Durability	Persists messages on disk for reliability.
Fault Tolerance	Resilient to node or system failures within the cluster.
Real-time Processing	Capable of processing data with minimal latency.

Q8. How does Kafka ensure message durability? (Data Integrity & Reliability)

Kafka ensures message durability through the following mechanisms:

• Replication: Kafka replicates data across multiple brokers to prevent data loss in the case of a broker failure.
• Persistent Storage: Kafka writes all data to disk, which means that messages are not lost even if the system crashes.
• Acknowledgements: Producers can wait for acknowledgements. A message is considered "committed" when all in-sync replicas for the partition have applied it to their log.
• Log Compaction: Kafka offers log compaction, which ensures that even if a topic retains only the last value for each key, the data will persist indefinitely.

Q9. Explain what is meant by Kafka Streams. (Stream Processing)

Kafka Streams is a client library for building applications and microservices where the input and output data are stored in Kafka clusters. It allows you to build applications that do stream processing on data flowing into and out of Kafka. Kafka Streams combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka’s server-side cluster technology.

Key concepts of Kafka Streams include:

• Stream: A sequence of data records, where each record is a key-value pair.
• Table: A changelog stream, where each data record represents an update. Essentially, it is the stream equivalent of a table in a relational database.

Q10. How would you handle data reprocessing in Kafka? (Data Processing & Fault Tolerance)

Handling data reprocessing in Kafka can involve several strategies, such as:

• Using the seek API to move the consumer’s offset back and reprocess data from a specific point.
• If exactly-once processing is required, using Kafka’s transactional APIs to ensure that messages are processed once and only once, even in the event of failures.
• Implementing idempotence on the consumer side to handle any duplicates that may arise from reprocessing.

How to Answer:

When discussing how you would handle data reprocessing in Kafka, you should display your understanding of Kafka’s fault tolerance mechanisms and your ability to apply them to ensure data integrity.

Example Answer:

To handle data reprocessing in Kafka, I would assess the scenario and choose the appropriate strategy. For instance, if I need to reprocess data from a specific point, I would modify the consumer’s offset using the seek API. This can be achieved by setting the offset to an earlier value, which would cause Kafka to replay messages from that point onwards.

Here’s a pseudo-code snippet demonstrating this:

consumer.subscribe(Arrays.asList("my-topic"));
consumer.poll(0); // ensures subscription
consumer.seekToBeginning(TopicPartition("my-topic", partitionToReprocess));

while (true) {
    ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
    for (ConsumerRecord<String, String> record : records) {
        processRecord(record);
    }
    consumer.commitSync();
}

In cases where exact processing is required, I would enable Kafka’s exactly-once semantics by configuring the producer and consumer with enable.idempotence set to true and using transactional APIs. This ensures that messages are neither lost nor duplicated in the event of reprocessing after a failure.

For handling duplicates, I would make the processing idempotent. This means that even if a message is processed more than once, the result would be the same as if it were processed only once. This approach often involves tracking which messages have been processed, either by storing message offsets or by making the operation itself idempotent, such as updating a database where the key is the message ID.

Q11. What are the different types of Kafka APIs? (API Knowledge)

Apache Kafka supports several APIs that allow developers to interact with Kafka in different ways. The main Kafka APIs include:

• Producer API: Allows applications to send streams of data to topics in the Kafka cluster.
• Consumer API: Enables applications to read streams of data from topics in the Kafka cluster.
• Streams API: This high-level API enables building applications that can process data in real-time. It supports stateful operations, windowing and joining streams.
• Connector API: This API is used to build and run reusable producers or consumers that connect Kafka topics to existing applications or data systems. For example, a database connector might capture every change to a database.
• AdminClient API: Used to manage and inspect topics, brokers, and other Kafka objects.

Each of these APIs is designed for specific use cases and enables Kafka to function as a versatile messaging and streaming platform.

Q12. How does Kafka handle failure and recovery? (Fault Tolerance & Recovery)

Apache Kafka is designed to be fault-tolerant and provides several mechanisms to handle failure and ensure data recovery:

• Replication: Kafka replicates data across multiple brokers. If one broker fails, the data can still be accessed from another replica.
• Leader and Follower: Each partition has one leader and multiple followers. The leader handles all read and write requests for the partition, while the followers replicate the leader’s data. If the leader fails, one of the followers can be promoted to be the new leader.
• Zookeeper Coordination: Kafka uses Zookeeper to manage cluster metadata and coordinate the distributed nature of the brokers. If a broker fails, Zookeeper helps in electing a new leader for the partitions that were led by the failed broker.
• Offset Management: Kafka consumers keep track of offsets to know which messages have been consumed. In case of failure, consumers can resume reading from the last committed offset.

In summary, Kafka’s design ensures that messages are not lost and that the system can continue to operate and recover from failures.

Q13. Describe the concept of log compaction in Kafka. (Data Management)

Log compaction is a feature in Kafka that helps to maintain a compacted log of data without losing any of the key information. The main points of log compaction include:

• Keyed Messages: Log compaction works on topics that have messages with a key. It ensures that for each key, at least the latest value is retained.
• Cleanup Process: Kafka periodically compacts the log by removing older records for a key when a newer record with the same key is present.
• Use Case: It is especially useful for restoring state after a restart or crash, as the compacted log contains all the necessary historical information for each key.

Log compaction ensures that Kafka topics can be used as a source of truth for data by retaining the full history of updates for each key.

Q14. How can you secure a Kafka cluster? (Security)

Securing a Kafka cluster involves several steps:

• Authentication: Control who can access the Kafka cluster. Kafka supports multiple mechanisms for authentication like SSL/TLS certificates and SASL (Simple Authentication and Security Layer) for clients and brokers.
• Authorization: Define what authenticated users and applications are allowed to do. Kafka uses ACLs (Access Control Lists) to specify which users have Read, Write, or Admin rights over specific topics.
• Encryption: Protect data in transit. Kafka supports SSL/TLS to encrypt data as it is transferred between clients and brokers to prevent eavesdropping.
• Auditing: Keep track of who did what and when. Integrating Kafka with an auditing system can help for compliance with various regulations.

Implementing these security features helps in protecting the Kafka cluster from unauthorized access and data breaches.

Q15. What is the purpose of Zookeeper in a Kafka ecosystem? (Cluster Coordination)

Zookeeper plays a crucial role in a Kafka ecosystem for cluster coordination. Its purposes include:

• Managing Cluster State: It keeps track of the status of Kafka brokers and partitions.
• Leader Election: Zookeeper performs leader election for partitions when the current leader broker fails.
• Configuration Management: It stores configuration information for topics and brokers.
• Access Control Lists: Zookeeper also stores ACLs for Kafka, which controls who can read or write to topics.

In summary, Zookeeper is responsible for the overall health and coordination of the Kafka cluster, ensuring high availability and consistency.

Q16. How do you monitor Kafka performance and what tools do you use? (Monitoring & Tools)

To monitor Kafka performance, there are several metrics that are important to keep an eye on, including:

• Broker-level metrics: Such as byte rates, request rates, and queue sizes.
• Topic-level metrics: Like message throughput and log sizes.
• Consumer-level metrics: Including consumer lag, which indicates how far behind consumers are in processing messages.
• Producer-level metrics: For monitoring the rate of message production and error rates.

Monitoring these metrics helps in identifying bottlenecks, ensuring high availability, and maintaining the overall health of the Kafka cluster.

Tools I use for monitoring Kafka:

• JMX tooling: Kafka exposes metrics through JMX (Java Management Extensions), and tools like JConsole or VisualVM can be used for ad-hoc monitoring.
• Prometheus and Grafana: For more robust monitoring, I use Prometheus to scrape Kafka metrics, which are then visualized through Grafana dashboards.
• Confluent Control Center: Part of the Confluent Kafka distribution, this tool provides comprehensive monitoring and management capabilities.
• Datadog/Kafka Manager/Burrow: These are third-party tools that provide additional monitoring and alerting capabilities.

Each of these tools can be set up to alert in case certain thresholds are breached, ensuring proactive performance management.

Q17. How can you scale a Kafka cluster? (Scalability)

Scaling a Kafka cluster can be done either by scaling up (adding more resources to existing machines) or scaling out (adding more machines to the cluster). Here’s how scaling is typically approached:

• Scaling Out (Horizontally): This is the most common approach to scaling a Kafka cluster, where you add more brokers to the cluster. This helps in distributing the load across a larger number of machines. It involves the following steps:

  1. Provision new broker(s) with the same configuration as existing ones.
  2. Expand the Kafka cluster by updating the broker list and ensuring the new brokers join the cluster.
  3. Rebalance the partitions across all brokers, which can be done using Kafka’s built-in tools such as kafka-reassign-partitions.sh.

• Scaling Up (Vertically): This method involves adding more resources such as CPU, RAM, or disk space to existing brokers. It is generally less preferred due to its limitations in effectively handling increased load as compared to horizontal scaling.

• Partitioning: Increasing the number of partitions for a topic can also help in scaling the Kafka cluster by allowing more parallelism in processing and increasing the throughput.

It’s important to note that while scaling out, you should also consider increasing the replication factor to maintain or improve fault tolerance.

Q18. Explain how you would troubleshoot a Kafka broker that is not starting. (Problem-Solving & Troubleshooting)

When a Kafka broker is not starting, I would follow these troubleshooting steps:

1. Check the broker logs: The first step is to look at the logs, which can be found in the directory specified by the log.dirs property in the broker configuration file. The logs often contain exceptions or errors that provide clues about the failure.
2. Validate configuration files: Ensure that the server.properties file doesn’t have any syntax errors and all the necessary configurations are correctly set.
3. Check for port conflicts: Verify that the ports specified for Kafka are free and not being used by another process.
4. Inspect the file system: If there’s an issue with the file system where the Kafka logs are stored, it can prevent the broker from starting. Check for disk space issues, permissions, or corruption.
5. Examine network settings: Ensure that the network settings and connectivity between brokers are correctly configured and that there are no firewall rules blocking communication.
6. JVM Options: If you’ve customized JVM settings, validate that these are not causing the broker to crash on startup.

Q19. How would you handle schema evolution in Kafka? (Schema Management)

Schema evolution in Kafka refers to the ability to update the schema used for messages over time without breaking backward compatibility. To handle schema evolution properly, consider the following practices:

• Use a Schema Registry: This allows you to manage and enforce schemas across your producers and consumers. Confluent Schema Registry is a popular choice.
• Backward Compatibility: Ensure that new schema versions are backward compatible with old ones. This means that new fields should have defaults or be optional.
• Forward Compatibility: Sometimes, it’s also important to maintain forward compatibility so that older schemas can read data written by clients using newer schemas.
• Versioning: Always version your schemas so that the consumers know which version of the schema to apply to read the data.

An example of schema evolution might involve adding a new optional field with a default value to an existing schema. Consumers using the old schema ignore the new field, while consumers using the new schema know to expect it and how to process it.

Q20. What are Kafka Connect and Kafka MirrorMaker, and when would you use them? (Data Integration)

Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other data systems. It provides a framework for building reusable producer and consumer APIs. You would use Kafka Connect when:

• You need to ingest data from databases, key-value stores, search indexes, or file systems into Kafka topics.
• You want to export data from Kafka topics into secondary indexes, storage systems, or real-time analytics systems.

Example configuration for Kafka Connect source connector that ingests data into Kafka:

name=local-file-source
connector.class=FileStreamSource
tasks.max=1
file=test.txt
topic=connect-test

Kafka MirrorMaker is a standalone tool used to replicate data between two Kafka clusters. It is often used for:

• Disaster Recovery: To replicate data across geographically distributed data centers.
• Aggregation: To aggregate data from multiple clusters into a central cluster for large-scale analytics.

Example command to start Kafka MirrorMaker:

bin/kafka-mirror-maker.sh --consumer.config sourceClusterConsumerConfig.properties --num.streams 2 --producer.config targetClusterProducerConfig.properties --whitelist=".*"

When choosing between Kafka Connect and Kafka MirrorMaker, use Kafka Connect for data integration with systems other than Kafka and use Kafka MirrorMaker for replicating data between Kafka clusters.

Q21. Can you explain the concept of exactly-once semantics in Kafka? (Messaging Semantics)

Exactly-once semantics (EOS) in Kafka ensures that each message is delivered once and only once. In other words, even if a producer sends a message multiple times due to retries, the message is processed only once by the end consumer. This avoids duplicate processing of messages, which is crucial in many applications, particularly those dealing with financial transactions or inventory management.

To achieve exactly-once semantics, Kafka combines idempotence and transactional messaging:

• Idempotence: Kafka producers can be configured to be idempotent, which ensures that even if a message is sent multiple times due to retries, it will be written to the log only once. This is achieved by assigning a unique sequence number to each message and the broker will only write a message with the same key and sequence number once.

• Transactional Messaging: Producers can send messages in transactions. Messages sent within the same transaction will either all be committed or all be aborted. This extends exactly-once semantics across multiple partitions and topics.

Implementation of exactly-once semantics can introduce additional overhead, as it requires more coordination between the producer, broker, and consumer. It’s important to weigh the benefits of EOS against the potential impact on throughput and latency.

Q22. Describe a situation where you optimized Kafka’s performance and how you achieved it. (Performance Tuning)

How to Answer
To answer this question, outline a specific situation where you identified a performance bottleneck and the steps you took to address it. Be sure to discuss the before-and-after scenario, highlighting the improvements in performance metrics.

Example Answer
In one of my previous roles, we were experiencing high latency in our Kafka consumer processing times. After an initial analysis, I discovered that the consumer instances were spending a significant amount of time waiting for I/O operations due to a small fetch size and default settings that were not optimized for our workload.

To optimize Kafka’s performance, I took the following steps:

• Increased the fetch.min.bytes configuration to ensure that the consumers were fetching larger batches of records, thus reducing the number of round trips to the broker.
• Adjusted the fetch.max.wait.ms to allow the broker more time to accumulate data before responding to the consumer fetch request.
• Tuned the max.partition.fetch.bytes setting to match our average message size, ensuring that we were utilizing the network bandwidth efficiently.

After these changes, we noticed a 40% reduction in consumer latency and an overall increase in throughput. Furthermore, by monitoring the consumer lag, we were able to verify that our consumers were keeping up with the production rate much better than before.

Q23. How do you configure Kafka for high throughput? (Configuration & Optimization)

To configure Kafka for high throughput, you would typically adjust both producer and broker configurations to maximize efficiency. Here is a list of common configurations to consider:

• Batch size: Increase the batch.size on the producer to allow more messages to be sent in a single batch, reducing the overhead per message.
• Linger time: Adjust the linger.ms to allow the producer to wait a bit longer to accumulate more messages for a batch before sending.
• Compression: Enable message compression on the producer (e.g., compression.type set to gzip, snappy, or lz4) to reduce the size of the batches sent over the network.
• Number of partitions: Increase the number of partitions for a topic to distribute the load across more brokers and to allow more parallelism on the consumer side.
• Replication factor: Be mindful of the replication factor; while a higher replication factor can improve fault tolerance, it can also increase write latency and reduce throughput.
• Log flush policy: Adjust broker configurations like log.flush.interval.messages and log.flush.interval.ms to control how often data is written to disk.

It is important to monitor performance and adjust these settings iteratively, as the optimal configuration can vary based on individual use-cases and hardware capabilities.

Q24. What is the role of a Kafka Controller? (Cluster Management)

The Kafka Controller is a critical component in a Kafka cluster responsible for maintaining the overall health of the cluster. Specifically, the controller has the following roles:

• Leadership Election: The controller is responsible for electing partition leaders, ensuring partitions are available for producers and consumers.
• Cluster Membership: It manages the list of active and live brokers in the cluster.
• Replica Management: The controller oversees the distribution of replicas across the brokers, ensuring that the configured replication factor is maintained.
• Topic Configuration Changes: Handles any changes to topic configurations, including partition reassignment.
• Cluster Metadata Updates: It propagates metadata updates to all other brokers so that they can maintain a consistent view of the cluster.

Only one broker in the cluster acts as the controller at any given time. If the controller broker fails, a new controller will be elected from the live brokers to take over these responsibilities.

Q25. Describe a challenging problem you solved with Kafka. (Problem-Solving & Experience)

How to Answer
When answering this question, describe a complex issue you encountered when working with Kafka, the steps you took to investigate, and the solution you implemented. Reflect on the impact of the resolution.

Example Answer
In a project I worked on, we had an issue where Kafka consumers were sporadically missing messages, leading to inconsistent data processing. After a thorough investigation, I identified the root cause as a combination of consumer rebalancing and offsets being prematurely committed.

The problem was addressed by implementing a custom offset management strategy. Instead of auto-committing offsets, we switched to manual offset commits and only committed an offset after ensuring that all messages up to that point had been fully processed and persisted downstream. This strategy ensured that no messages were skipped even if a rebalance occurred.

Additionally, I tuned the session.timeout.ms and max.poll.interval.ms settings to more appropriate values that matched our processing times, thus reducing the frequency of unnecessary rebalances.

This solution stabilized our message processing and led to a significant reduction in data discrepancies. Our system’s reliability improved, which was critical for our data analytics platform.

4. Tips for Preparation

To excel in a Kafka interview, you should first dive deep into Kafka’s documentation. This ensures that you understand its core concepts, architecture, and APIs. Additionally, practical experience is invaluable. Set up a Kafka instance and experiment with producing and consuming messages, setting up topics, and partitions.

Stay abreast of Kafka’s latest features and updates, and familiarize yourself with stream processing. Soft skills are also vital; be prepared to demonstrate how you’ve solved problems or worked in a team setting. Finally, think of scenarios where you’ve shown leadership, as these examples can often set you apart from other candidates.

5. During & After the Interview

In the interview, clarity and confidence are key. Communicate your thoughts succinctly and demonstrate your understanding of Kafka with practical examples. Interviewers often look for candidates who can articulate complex concepts clearly and apply theoretical knowledge practically.

Avoid getting bogged down in jargon without explanation, and don’t be afraid to ask for clarification on questions. At the end of the interview, asking insightful questions about the company’s Kafka use cases or challenges shows engagement and foresight. Afterward, send a thank-you email to express your appreciation for the opportunity and to reinforce your interest in the role.

Reflect on the interview to identify areas for improvement, and don’t hesitate to politely follow up if you haven’t received feedback within the communicated timeline.