Kafka Developers Hiring Guide

An open-source distributed event streaming platform that promises zero downtime  

Kafka is an open-source distributed message streaming platform that provides an efficient way for storing and subscribing event data for applications. It consists of cluster nodes that import and replicate data that different applications can later access. It can process hundreds of thousands of messages both online and offline. It guarantees zero downtime and zero data loss for the system.

Kafka is highly reliable due to its partitioning. The execution time in Kafka is constant, i.e., O(log N). That means the algorithm execution is independent of the message’s input size. It can also balance and support multiple subscribers. Kafka is also fault-tolerant – it can handle failures by restarting the server by itself.

  • Hiring Guide

  • Interview Questions

  • Job Description

Kafka has several components such as producers, consumers, topics, clusters, replicas, and partitions. Producers send messages to Kafka clusters, and consumers read messages from them. The messages are stored in topics. Kafka splits these topics into partitions. In partitions, all messages are ordered linearly, and you can check particular messages through their offset. 

Producers perform load balancing to make sure that messages are divided evenly across partitions. If a consumer drops off, the consumer group rebalances the partition between remaining consumers. Kafka works on exactly-once semantics, i.e. all data that goes through it can only be processed once. 

Data in Kafka is distributed and streamlined over a cluster of nodes to handle large amounts of data. Its distributed commit log carries messages to the disk as fast as possible, making it efficient for data transfer. It’s fast and can handle several types of clients. You can also use it to transform, aggregate, and filter data. 


Kafka in Today’s Industry

Many companies, such as LinkedIn, Yahoo, and Pinterest use Kafka. Kafka has many industry use cases such as processing payments, collecting customer interactions, tracking metrics, and processing data streams.  

Kafka can handle messages with large volumes of data streams. If required, Kafka can also scale in many different dimensions, i.e. you can increase the numbers of processors, consumers, or producers, whichever suits your business requirements. Kafka is stable and has high throughput for both publishing and subscribing messages. 

Kafka can also process real-time data through Kafka Streams. It’s a client library that allows you to work with continuously updating datasets. Stream processors take input from streams and apply their own processes to it. It has a low barrier to entry and can create small-scale applications for proof of concepts. These applications can later be scaled as per requirements.  


Issues in finding the best Kafka Developer

Even though you may hire the best Kafka engineers, they may not have adequate experience regarding the hardware requirements for Kafka implementation. Inexperienced engineers sometimes may overestimate the hardware requirements for Kafka. This results in clients investing in expensive hardware that’s unnecessary for their projects. A good engineer should gauge the scale of data the client wants to run through Kafka and develop a systematic hardware plan for optimal data processing. 

Due to the large amount of data going through Kafka per second, the system may sometimes back up, and problems may arise. It may have several issues - the leader may break, or the brokers may crash. Issues like these need to be addressed as soon as possible. 

Unfortunately, finding a Kafka specialist who can understand these issues and fix them ASAP isn’t easy. Even though the system is fault-tolerant, Kafka engineers should understand common Kafka failures and ensure that such events don’t hamper message consumption. 


How to choose the best Kafka developer

The perfect Kafka specialist should have proficiency in programming languages such as Java, Golang, Dot Net, and Python. They should be able to integrate Kafka with Hadoop, Spark, and Storm, and they should be able to implement Kafka for client's applications. 

A Kafka expert should also understand hardware requirements for a particular project, such as the CPU/RAM, the type and number of drives, network type, and file systems, among others. All this hardware is immensely significant if you want to develop optimal functioning Kafka architecture. 

Kafka experts should also be able to advise their clients on which cloud providers they should choose based on their network requirements. The network bandwidth may be a significant hold back for the proper functioning of Kafka, so knowing everything about cloud providers is key for a seasoned Kafka engineer. 


Conclusion

Kafka has become one of the most popular platforms for message streaming. It’s fast, scalable, reliable, and boasts high performance. As a result of its increasing popularity, it has allowed many consumers worldwide to implement an efficient system for large-scale data processing.

Here are some questions you can ask Kafka developers before hiring them:


What are some of the core Kafka APIs and what are their functions?

Here is a list of Core Kafka APIs and their list of functions:

  • Admin API : Used to monitor topics, brokers, and configurations.
  • Producer API: Publishes streams of data from applications to Kafka topics in the Kafka clusters.
  • Consumer API: Reads streams of data from one or more topics.
  • Streams API: Implements stream processing microservices and applications for continuous data.
  • Connect API: Builds and runs connectors that read or write streams from external systems.

Why does Kafka use ZooKeeper?

Kafka uses ZooKeeper to manage topics, store offsets of messages, and keep track of cluster nodes. A Kafka professional should know the number of ZooKeepers required for the proper functioning of Kafka nodes, depending on the workload. At most, 5 Zookeepers should be used in one environment.


Can Kafka’s redundancy feature create a problem for clients? And what solution can you offer for it?

Too many redundant copies of data in Kafka will affect its performance and increase storage costs. The optimal solution for clients would be to use Kafka to store data tentatively and later migrate the data to a different database. This should reduce overhead costs and improve performance.


What are some of Kafka's system tools and their functions?

  • Mirror Maker: These tools help to mirror clusters, i.e., replicate Kafka clusters. Data is copied from a topic and written to the subsequent topic in the destination cluster.
  • Kafka Migration Tool: This tool allows seamless movement of brokers from one version to another. It’s a reliable tool that enables easy and efficient synchronization of data among different environments.
  • Consumer Offset Checker: This is an essential tool used for debugging clients. It also helps check the efficiency of the mirroring cluster.

Explain the role of the offset.

Messages in the partitions have a unique ID number called the offset. It uniquely identifies different messages in the partitions.

We are looking for a highly qualified Kafka developer to join our team to design and develop large-scale software. We are looking for smart team players who can code and maintain medium-to-large applications. The developer must also be good in documentation and should be able to meet deadlines. If you’re a goal-oriented developer, it’s an excellent opportunity for you to showcase your skills.


Responsibilities

  • Write reusable and reliable web applications. 
  • Create in-house and client projects based on Spring boot-microservices for Kafka setup. 
  • Set up production and testing Kafka environments
  • API implementation for Spark and Spring calls.   
  • Improve performance and functionality of systems and decrease latency.
  • Implement data movement from and to HDFS from different sources.  
  • Coordinate with internal and external teams to understand business requirements
  • Follow best industry practices and standards
  • {{Add other relevant responsibilities}}

Skills and Qualifications

  • Knowledge of Java and Golang. Also should have prior experience with Kafka. 
  • Experience in designing reusable code and modules using Zookeeper, Streams, and brokers
  • Understanding of JDBC, JMS, and MQ.
  • Proven experience with Kafka Rest Proxy
  • Experience with Kafka Converters.
  • Experience with redundancy tools, cluster tools, and monitoring tools. 
  • Knowledge of RDBMS, Hadoop ecosystem, alert setup. 
  • Problem-solving skills and team spirit 
  • {{Add other frameworks or libraries related to your development stack}} 
  • {{List education level or certification required}}

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