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Apache Kafka
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Ihor K, Apache Kafka Developer

Last Updated: 3 Jun 2024
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- Data Engineer with a Ph.D. degree in Measurement methods, Master of industrial automation - 16+ years experience with data-driven projects - Strong background in statistics, machine learning, AI, and predictive modeling of big data sets. - AWS Certified Data Analytics. AWS Certified Cloud Practitioner. - Experience in ETL operations and data curation - PostgreSQL, SQL, Microsoft SQL, MySQL, Snowflake - Big Data Fundamentals via PySpark, Google Cloud, AWS. - Python, Scala, C#, C++ - Skills and knowledge to design and build analytics reports, from data preparation to visualization in BI systems.

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Data Pipelines (ETL)

Data Pipelines (ETL)

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Nattiq, Apache Kafka Developer

Last Updated: 3 Jun 2024

- 12+ years experience working in the IT industry; - 12+ years experience in Data Engineering with Oracle Databases, Data Warehouse, Big Data, and Batch/Real time streaming systems; - Good skills working with Microsoft Azure, AWS, and GCP; - Deep abilities working with Big Data/Cloudera/Hadoop, Ecosystem/Data Warehouse, ETL, CI/CD; - Good experience working with Power BI, and Tableau; - 4+ years experience working with Python; - Strong skills with SQL, NoSQL, Spark SQL; - Good abilities working with Snowflake and DBT; - Strong abilities with Apache Kafka, Apache Spark/PySpark, and Apache Airflow; - Upper-Intermediate English.

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Apache Kafka

Apache Kafka


Python   4 yr.

Azure (Microsoft Azure)

Azure (Microsoft Azure)   5 yr.

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Andrii P., Apache Kafka Developer

Last Updated: 29 Aug 2023

- 10+ years of experience in software development using Java/JavaEE stack - Proficient in utilizing a wide range of technologies and tools, such as Spring, Elasticsearch, Apache Kafka, Apache Cassandra, Maven, JUnit/Mockito, Jenkins, Kubernetes, Docker, Git, Hibernate, Play framework, and more. - Skilled in code development, task prioritization, and documentation maintenance. - Mentored junior developers, performed unit and functional testing, and provided documentation maintenance throughout various projects. - Participated in the full life cycle of two large enterprise projects - Practical experience with systems-level designing - Upper-Intermediate English

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Apache Kafka

Apache Kafka   3 yr.



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Roman F., Apache Kafka Developer

Last Updated: 3 Jun 2024
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- Experienced Software Engineer with 8 years of commercial experience in demanding environments, focused on producing cutting-edge systems for businesses. - Skilled in infrastructure cost optimization, Kubernetes, and Terraform. - Proficient in PHP, JavaScript, Golang, and various databases such as MySQL, PostgreSQL, and MongoDB. - Extensive experience with Amazon Web Services (AWS) and containerization technologies like Docker. - Strong background in computer science with a Master's degree in Computer Science. - Familiar with Agile and Scrum methodologies, as well as software development practices and SDLC. - Excellent communication skills and fluent in Ukrainian with advanced English proficiency.

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Apache Kafka

Apache Kafka



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Alexey K., Apache Kafka Developer

Kyiv, Ukraine
Last Updated: 4 Jul 2023

- 20+ years of software development - 5+ years of Python & Elixir, Go development, .Net development - Mathematical Science education (diploma with honours), R language for statistical computing and graphics - Strong knowledge in Python, Go, Redis, Elixir, ASP.NET MVC, Angular, DevExpress, C++, PHP, MS SQL Server, PostgreSQL, Oracle SQL, NodeJS, React, React Native, Azure, AWS, Apache Kafka - Strong knowledge of ERP Systems - Strong web technologies - Extensive knowledge of software development lifecycle - Available in approx 4 weeks

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Table of Contents

Pros & cons of Apache Kafka

Pros & cons

9 Pros of Apache Kafka

  • Scalability: Apache Kafka is designed to handle high-volume, high-throughput data streams. It can handle millions of messages per second and can scale horizontally across multiple brokers.
  • Durability: Kafka provides fault-tolerant storage for your data. Messages are persisted to disk, ensuring data durability even in the event of failures.
  • High throughput: Kafka is built to handle high-velocity data streams. It can efficiently process and deliver large volumes of data in real-time.
  • Low latency: Kafka offers low message latency, ensuring that data is delivered quickly and efficiently.
  • Reliability: Kafka guarantees message delivery by maintaining a durable log of messages. It provides strong durability and fault-tolerance mechanisms.
  • Real-time data processing: Kafka enables real-time processing of streaming data, allowing for immediate analysis and decision-making based on the latest data.
  • Integration: Kafka integrates well with other data systems, allowing you to easily connect and exchange data between different applications and platforms.
  • Message replay: Kafka allows you to replay messages from the beginning, making it easier to debug and analyze data in case of issues or errors.
  • Flexible architecture: Kafka’s architecture is flexible and can be adapted to various use cases, such as event streaming, data integration, and microservices communication.

9 Cons of Apache Kafka

  • Complex setup: Setting up and configuring Kafka can be challenging, especially for those who are new to distributed systems.
  • Learning curve: Working with Kafka requires some level of knowledge and understanding of distributed systems and event-driven architectures.
  • Operational complexity: Running and managing Kafka clusters can be complex and resource-intensive, requiring dedicated operational expertise.
  • Storage requirements: Kafka relies on disk storage for message persistence, which can lead to high storage requirements, especially for long retention periods.
  • Monitoring and maintenance: Monitoring and maintaining Kafka clusters require ongoing effort to ensure optimal performance and reliability.
  • Hardware requirements: Kafka’s high throughput and low latency capabilities may require significant hardware resources to handle the processing load.
  • Single point of failure: Although Kafka provides fault-tolerance mechanisms, a single point of failure can still impact the overall system’s availability.
  • Steep learning curve for developers: Developers need to learn Kafka-specific APIs and concepts to effectively build applications that integrate with Kafka.
  • Complex security setup: Kafka’s security features, such as authentication and authorization, can be complex to set up and manage.

Cases when Apache Kafka does not work

Does not work
  1. Insufficient Hardware Resources: Apache Kafka requires sufficient hardware resources to handle the volume of data and the number of concurrent connections. If the hardware resources are not adequate, it may lead to performance issues or even failure of the Kafka cluster.
  2. Network Connectivity Issues: Kafka heavily relies on network communication between its brokers, producers, and consumers. If there are network connectivity issues, such as high latency, packet loss, or network congestion, it can impact the overall functionality and performance of Kafka.
  3. Incorrect Configuration: Kafka has various configuration parameters that need to be set correctly for optimal performance. If the configuration is not properly tuned or if there are errors in the configuration file, it can result in Kafka not functioning as expected.
  4. Storage Limitations: Kafka relies on disk storage to persist messages and maintain its log. If the available disk space is insufficient, Kafka may encounter errors or fail to store incoming messages, leading to data loss or system instability.
  5. Software Compatibility Issues: Kafka integrates with other software components, such as ZooKeeper, to manage its cluster and metadata. If there are compatibility issues between Kafka and these components, it can cause Kafka to malfunction or fail.
  6. Security Configuration: Kafka provides security features like SSL/TLS encryption and authentication mechanisms. If the security configuration is not set up correctly or if there are issues with certificates or authentication, Kafka may not work properly or may deny access to clients.
  7. Software Bugs: Like any software, Kafka can have bugs or issues that can impact its functionality. These bugs can arise due to programming errors, compatibility issues, or other factors. It is important to keep Kafka up to date with the latest bug fixes and patches to avoid running into known issues.
  8. Operational Errors: Human errors, such as misconfiguration, improper deployment, or incorrect handling of Kafka components, can also lead to Kafka not working as expected. It is crucial to follow best practices and guidelines while operating and managing Kafka to minimize the risk of operational errors.

Soft skills of a Apache Kafka Developer

Soft skills

Soft skills are essential for Apache Kafka Developers as they work in a collaborative and dynamic environment. These skills complement technical expertise and enable developers to effectively communicate, collaborate, and lead teams. Here are the soft skills required for Apache Kafka Developers at different levels:


  • Effective Communication: Ability to communicate ideas, issues, and solutions clearly and concisely to team members and stakeholders.
  • Problem-Solving: Aptitude for identifying and resolving issues, and the ability to think critically and analytically.
  • Adaptability: Willingness to learn and adapt to new technologies, tools, and methodologies.
  • Teamwork: Ability to work collaboratively in a team environment, contribute to group discussions, and support team goals.
  • Time Management: Capacity to prioritize tasks, meet deadlines, and manage time efficiently.


  • Leadership: Ability to take ownership of projects, guide junior team members, and provide technical mentorship.
  • Decision Making: Capability to make informed decisions based on business requirements, technical feasibility, and project goals.
  • Conflict Resolution: Skill in resolving conflicts, handling disagreements, and fostering a positive work environment.
  • Project Management: Proficiency in managing and coordinating tasks, resources, and timelines to ensure successful project delivery.
  • Collaboration: Capacity to work with cross-functional teams, stakeholders, and external partners to achieve project objectives.
  • Presentation Skills: Ability to deliver clear and engaging presentations to technical and non-technical audiences.
  • Attention to Detail: Thoroughness in reviewing code, configurations, and documentation to ensure accuracy and quality.


  • Influence and Persuasion: Ability to influence and persuade stakeholders, team members, and executives to adopt best practices and strategies.
  • Strategic Thinking: Capability to align technical decisions with business objectives and contribute to long-term planning.
  • Empathy: Skill in understanding the perspectives and needs of team members, stakeholders, and end-users.
  • Knowledge Sharing: Willingness to share knowledge, mentor others, and contribute to the growth of the team and organization.
  • Critical Thinking: Advanced ability to analyze complex problems, evaluate multiple solutions, and make data-driven decisions.
  • Networking: Proficiency in building professional relationships, participating in industry events, and staying updated with the latest trends.
  • Resilience: Ability to handle pressure, navigate challenges, and adapt to changes in project scope or requirements.

Expert/Team Lead

  • Strategic Leadership: Ability to develop and communicate a clear technical vision, set goals, and drive the team towards success.
  • Collaborative Decision Making: Skill in facilitating discussions, gathering input, and making informed decisions as a team.
  • Change Management: Proficiency in leading teams through organizational changes, process improvements, and technology migrations.
  • Mentorship: Capability to provide guidance and mentorship to junior and mid-level developers, fostering their professional growth.
  • Conflict Management: Advanced skill in handling conflicts, negotiating resolutions, and maintaining a positive team dynamic.
  • Business Acumen: Knowledge and understanding of business operations, market trends, and customer needs to drive strategic initiatives.
  • Innovation: Ability to identify opportunities for innovation, explore emerging technologies, and drive continuous improvement.
  • Risk Management: Skill in identifying and mitigating risks associated with technical decisions, project timelines, and resource allocation.
  • Communication Excellence: Exceptional ability to communicate complex technical concepts to both technical and non-technical stakeholders.
  • Team Building: Proficiency in building high-performing teams, fostering a collaborative culture, and recruiting top talent.
  • Client Relationship Management: Skill in managing client relationships, understanding their needs, and delivering exceptional solutions.

Hard skills of a Apache Kafka Developer

Hard skills

As an Apache Kafka Developer, you need to possess a range of hard skills to effectively work with the Kafka messaging system. Here are the hard skills required for different levels of expertise:


  • Apache Kafka fundamentals: Understanding the core concepts and architecture of Apache Kafka.
  • Producer and Consumer development: Building and configuring Kafka producers and consumers.
  • Stream processing: Knowledge of stream processing frameworks like Kafka Streams or Apache Flink.
  • Data serialization: Experience with data serialization formats such as Avro or JSON.
  • Monitoring and troubleshooting: Basic skills in monitoring Kafka clusters and troubleshooting common issues.


  • Advanced Kafka concepts: In-depth knowledge of Kafka topics, partitions, offsets, and replication.
  • Message delivery semantics: Understanding different message delivery semantics like at-most-once, at-least-once, and exactly-once.
  • Schema registry: Experience with schema registry tools for managing Avro schemas.
  • Performance optimization: Proficiency in optimizing Kafka performance and throughput.
  • Cluster management: Skills in managing and scaling Kafka clusters.
  • Data integration: Experience with integrating Kafka with other systems like Apache Spark or Apache Hadoop.
  • Security: Understanding Kafka security mechanisms like SSL encryption and authentication.


  • Kafka Connect: Extensive knowledge of Kafka Connect for data integration with external systems.
  • Advanced troubleshooting: Expertise in diagnosing and resolving complex Kafka issues.
  • Cluster optimization: Ability to fine-tune Kafka clusters for optimal performance.
  • High availability and fault tolerance: Skills in setting up Kafka clusters with high availability and fault tolerance.
  • Data governance: Understanding data governance principles and implementing them in Kafka.
  • Capacity planning: Experience in capacity planning for Kafka clusters and estimating resource requirements.
  • Disaster recovery: Knowledge of disaster recovery strategies for Kafka deployments.
  • Real-time data processing: Proficiency in real-time data processing using Kafka and related technologies.

Expert/Team Lead

  • Kafka ecosystem: Deep understanding of the entire Kafka ecosystem, including tools like Kafka Streams, KSQL, and MirrorMaker.
  • Architecture design: Ability to design scalable and fault-tolerant Kafka architectures.
  • Performance tuning: Expertise in fine-tuning Kafka performance for high-throughput and low-latency use cases.
  • Leadership skills: Experience in leading Kafka development teams and guiding junior developers.
  • Integration patterns: Extensive knowledge of different integration patterns and best practices for Kafka integration.
  • Security hardening: Skills in hardening Kafka deployments and implementing advanced security measures.
  • Continuous integration and deployment: Proficiency in setting up CI/CD pipelines for Kafka applications.
  • Big data ecosystem: Understanding of the broader big data ecosystem and how Kafka fits into it.
  • Event-driven architecture: Knowledge of event-driven architecture principles and designing event-driven systems using Kafka.
  • Cloud platforms: Experience in deploying and managing Kafka on cloud platforms like AWS, Azure, or GCP.
  • Performance benchmarking: Ability to conduct performance benchmarking and load testing for Kafka deployments.

TOP 10 Apache Kafka Related Technologies

Related Technologies
  • Java

    Java is the most widely used programming language for Apache Kafka development. It offers a rich set of libraries and frameworks that make it easy to integrate with Kafka. With its strong ecosystem and extensive community support, Java provides a reliable and scalable option for building Kafka applications.

  • Scala

    Scala is another popular language for Apache Kafka development. It combines object-oriented and functional programming paradigms, making it well-suited for building distributed and event-driven systems like Kafka. Its concise syntax and powerful features enable developers to write clean and expressive code.

  • Python

    Python is gaining popularity as a language for Kafka development due to its simplicity and ease of use. It offers a variety of Kafka client libraries that facilitate integration with Kafka clusters. Python’s extensive ecosystem and large community make it a convenient choice for rapid prototyping and data processing tasks.

  • Spring Kafka

    Spring Kafka is a powerful framework that provides seamless integration between Spring Boot and Apache Kafka. It simplifies the development of Kafka-based applications by offering abstractions for handling producers, consumers, and stream processing. With its intuitive API and robust features, Spring Kafka accelerates development productivity.

  • Kafka Streams

    Kafka Streams is a lightweight Java library that allows developers to build real-time stream processing applications on top of Kafka. It provides a high-level DSL for defining data transformations and aggregations, making it easy to process and analyze data in real-time. Kafka Streams eliminates the need for external stream processing frameworks.

  • Avro

    Avro is a data serialization framework that integrates well with Apache Kafka. It provides a compact binary format for efficient data storage and transmission. Avro schemas enable schema evolution and compatibility, allowing easy evolution of data structures over time. Its support for strong typing and schema evolution makes it a popular choice for Kafka development.

  • Kafka Connect

    Kafka Connect is a framework for building and running scalable data import/export pipelines between Kafka and external systems. It provides a standardized way to connect Kafka with various data sources and sinks, simplifying data integration and enabling seamless data movement. Kafka Connect offers a wide range of connectors for popular databases, file systems, and messaging systems.

TOP 10 Tech facts and history of creation and versions about Apache Kafka Development

Facts and history
  • Apache Kafka was created by LinkedIn in 2011, and it was initially developed by Jay Kreps, Neha Narkhede, and Jun Rao.
  • Kafka is based on the publish-subscribe messaging model, where producers publish messages to topics, and consumers subscribe to those topics to receive the messages.
  • Kafka was designed to handle real-time data streams and provide high-throughput, fault-tolerant, and scalable messaging.
  • The initial release of Kafka was made public in 2011, and it quickly gained popularity due to its ability to handle large-scale data streaming.
  • Kafka is written in Java and Scala, and it leverages the Apache ZooKeeper coordination service for distributed messaging.
  • In 2012, Kafka became an Apache Software Foundation project and was renamed Apache Kafka.
  • Kafka introduced the concept of log compaction, which allows the system to retain only the latest value for each key in a topic, enabling efficient data retention and replayability.
  • The first stable release of Apache Kafka (version was launched in 2015, introducing significant improvements in performance, reliability, and security.
  • Apache Kafka has a strong ecosystem, with connectors for various data sources and sinks, stream processing frameworks like Apache Flink and Apache Samza, and integration with popular data storage systems like Apache Hadoop and Apache Cassandra.
  • The latest major release of Apache Kafka is version 2.7.0, which was released in January 2021, bringing enhancements such as improvements in Kafka Streams, better security features, and updates to the Kafka Connect API.

What are top Apache Kafka instruments and tools?

Instruments and tools
  • Kafka Manager: Kafka Manager is a web-based tool for managing and monitoring Apache Kafka clusters. It provides a user-friendly interface for managing topics, partitions, and consumers. It was initially developed by Yahoo and is now maintained by the open-source community. Kafka Manager offers various features such as topic creation and deletion, partition reassignment, and consumer lag monitoring.
  • Confluent Control Center: Confluent Control Center is a comprehensive monitoring and management tool for Apache Kafka, developed by Confluent. It provides real-time visibility into the health and performance of Kafka clusters. Control Center offers features such as centralized cluster management, topic and consumer group monitoring, and alerting capabilities. It also provides historical data for analysis and troubleshooting.
  • Kafdrop: Kafdrop is a lightweight web-based tool for monitoring Kafka clusters. It offers a simple and intuitive interface to view Kafka topics, consumer groups, and their respective offsets. Kafdrop also provides the ability to view message contents and search for specific messages. It is known for its ease of setup and use, making it a popular choice among developers.
  • Kafka Streams: Kafka Streams is a client library for building real-time streaming applications on top of Apache Kafka. It enables developers to process and analyze data in real-time, leveraging the scalability and fault-tolerance of Kafka. Kafka Streams provides a high-level DSL (Domain-Specific Language) for building stream processing pipelines. It has gained popularity for its ability to seamlessly integrate with Kafka and its simplicity in building scalable and fault-tolerant stream processing applications.
  • Apache NiFi: Apache NiFi is an open-source data integration platform that supports Kafka as one of its data sources and destinations. NiFi provides a graphical interface to design and manage data flows, making it easy to ingest and transform data from various sources into Kafka. It offers powerful data routing, transformation, and enrichment capabilities, making it a versatile tool for integrating Kafka into complex data workflows.

How and where is Apache Kafka used?

How and where
Case NameCase Description
Real-time Stream ProcessingApache Kafka is well-suited for real-time stream processing applications. It allows organizations to process and analyze large volumes of data in real-time as it is ingested into the system. This is particularly useful in scenarios such as fraud detection, real-time analytics, and monitoring of IoT devices. Kafka’s distributed architecture and high-throughput capabilities enable efficient processing of streaming data, ensuring timely insights and actions.
Event-driven MicroservicesKafka’s publish-subscribe model makes it an ideal choice for implementing event-driven microservices architectures. It enables loosely coupled services to communicate asynchronously through the use of topics. Services can publish events to relevant topics, and other services can subscribe to those topics to consume the events. This decoupled communication style allows for scalability, fault tolerance, and flexibility in building complex, distributed systems.
Log AggregationApache Kafka is commonly used for log aggregation, where it serves as a centralized and scalable platform for collecting and storing log data from various sources. By consolidating logs from different systems and applications into Kafka topics, organizations can easily analyze and monitor system behavior, troubleshoot issues, and perform advanced analytics. Kafka’s fault-tolerant nature ensures that log data is reliably stored and accessible for as long as needed.
Commit Log for Distributed DatabasesKafka’s commit log architecture makes it an excellent choice for acting as a reliable and scalable commit log for distributed databases. By leveraging Kafka as a write-ahead log, databases can ensure durability and fault tolerance. Changes made to the database can be written to Kafka topics, providing a persistent record of all modifications. This allows for easier replication, recovery, and synchronization of data across multiple database instances.
Data Integration and ETL PipelinesKafka’s ability to handle large-scale data ingestion and its support for connectors make it a popular choice for building data integration and ETL (Extract, Transform, Load) pipelines. Organizations can use Kafka as a central hub for streaming data from various sources, such as databases, applications, and IoT devices, to downstream systems for processing, analysis, and storage. Kafka’s fault tolerance, scalability, and integration capabilities simplify the development of robust and scalable data pipelines.
Messaging and QueuingKafka’s core functionality as a distributed messaging system makes it suitable for building reliable and scalable messaging and queuing systems. Applications can produce messages to Kafka topics, and other applications can consume those messages in a fault-tolerant manner. This enables asynchronous communication, decoupling producers and consumers, and ensuring high throughput and low latency message processing. Kafka’s replication and partitioning mechanisms provide fault tolerance and scalability for messaging at scale.

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