Course Introduction
Apache Kafka Fundamentals for Data Professionals Training Course provides a practical foundation for professionals who need to understand, design, and work with event-driven data platforms. The course introduces Kafka as a distributed event streaming platform and explains how data professionals can use it to ingest, transport, process, integrate, and distribute continuously generated data across modern enterprise environments.
Participants develop a strong understanding of Kafka's core architecture, including brokers, clusters, topics, partitions, records, producers, consumers, offsets, and replication. The course explains how these components work together to support scalable data pipelines and reliable event processing. Learners also examine how partitions influence parallelism, ordering, scalability, and consumer workloads in practical streaming architectures.
The training focuses on producer and consumer development, helping participants understand how applications publish records to Kafka topics and how consuming applications retrieve and process those records. Participants explore serialization, deserialization, keys, partitioning, acknowledgments, offsets, consumer groups, rebalancing, and delivery semantics. These concepts provide the foundation for developing reliable Kafka-based data ingestion and processing workflows.
Data integration is another major component of the course. Learners explore Kafka Connect and understand how source and sink connectors can integrate Kafka with databases, files, cloud platforms, applications, and analytical systems. The course also introduces Kafka Streams and its role in building applications that process and transform event streams, enabling participants to understand how Kafka can support both data movement and stream-processing requirements.
The course emphasizes operational and reliability considerations that are important for production environments. Participants examine replication, fault tolerance, retention, log management, partition planning, monitoring, security, authentication, authorization, and performance considerations. The training also introduces practical troubleshooting approaches for common producer, consumer, connectivity, partition, offset, and cluster-related issues.
By completing the course, participants will be able to understand Kafka-based architectures, create and manage topics, develop basic producer and consumer workflows, configure consumer groups, integrate external systems, and evaluate streaming design decisions. The course is designed to give data professionals a practical understanding of Kafka's role in modern data engineering, real-time analytics, event-driven applications, and enterprise data integration.
Duration
5 days
Who Should Attend
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Data engineers building real-time and event-driven data ingestion pipelines.
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Data analysts who need to understand streaming data sources and Kafka-based architectures.
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Data architects designing modern event streaming and data integration platforms.
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Software developers developing applications that publish or consume Kafka events.
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Cloud engineers implementing Kafka-based data processing solutions in cloud environments.
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Big data professionals working with distributed data platforms and streaming architectures.
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Analytics engineers integrating streaming events with analytical data platforms.
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Database professionals working with change-data-capture and event-driven integration patterns.
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DevOps and platform engineers responsible for Kafka infrastructure and operational environments.
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Data scientists consuming real-time event streams for analytical and machine learning workflows.
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Technical consultants implementing Apache Kafka solutions for enterprise data projects.
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IT professionals seeking practical knowledge of event streaming and real-time data technologies.
Course Objectives
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Understand Apache Kafka architecture, core components, event streaming concepts, and the role Kafka plays in modern data engineering environments.
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Explain topics, partitions, records, brokers, offsets, replication, and consumer groups and how these components interact within Kafka clusters.
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Develop practical skills for creating Kafka producers that publish reliable event records while applying appropriate keys, serialization, and delivery configurations.
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Build and configure Kafka consumers that retrieve, process, commit, and manage records efficiently across individual and grouped consumption workloads.
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Understand consumer groups, partition assignment, rebalancing, offsets, and parallel processing when designing scalable Kafka consumption architectures.
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Apply appropriate retention, partitioning, replication, acknowledgment, and delivery-semantic strategies to improve Kafka workload reliability.
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Use Kafka Connect concepts to integrate Kafka with external databases, applications, files, cloud platforms, and analytical data destinations.
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Explore Kafka Streams concepts for transforming, filtering, aggregating, joining, and processing event streams within Kafka-based applications.
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Apply security, monitoring, troubleshooting, performance, and operational practices required for reliable Kafka environments and production data pipelines.
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Design practical Kafka architectures for real-time data ingestion, event-driven applications, streaming analytics, enterprise integration, and modern data engineering use cases.
Comprehensive Course Outline
Module 1: Apache Kafka Fundamentals and Event Streaming
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Introduction to event streaming, real-time data architectures, and the role of Apache Kafka in modern data platforms.
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Understanding Kafka clusters, brokers, topics, records, partitions, producers, consumers, offsets, and replication.
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Exploring common Kafka use cases including data integration, real-time analytics, event-driven applications, monitoring, and streaming pipelines.
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Comparing traditional batch-oriented integration with event streaming architectures and identifying appropriate Kafka implementation scenarios.
Module 2: Kafka Topics, Partitions, and Records
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Creating and managing Kafka topics while understanding topic configuration, naming conventions, retention, and lifecycle considerations.
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Exploring partitions and understanding how partitioning affects scalability, parallelism, ordering, throughput, and consumer processing.
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Understanding Kafka records, keys, values, timestamps, headers, offsets, and their importance in event-driven data workflows.
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Designing appropriate partition strategies based on workload volume, ordering requirements, consumer concurrency, and expected growth.
Module 3: Kafka Producers and Data Ingestion
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Understanding Kafka producer architecture and developing applications that publish records to appropriate Kafka topics.
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Configuring producer properties such as bootstrap servers, serialization, batching, compression, acknowledgments, and delivery-related settings.
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Applying record keys and partitioning strategies to control event distribution and maintain required ordering characteristics.
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Handling producer errors, retries, timeouts, delivery failures, and other operational considerations in reliable ingestion pipelines. (kafka.apache.org)
Module 4: Kafka Consumers and Consumer Groups
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Understanding Kafka consumer architecture, polling, offsets, subscriptions, partition assignments, and record processing workflows.
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Creating consumers that retrieve and process records while applying appropriate deserialization and offset-management strategies.
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Working with consumer groups to distribute partitions across multiple consumers for scalable and parallel event processing.
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Understanding rebalancing, committed offsets, consumer lag, processing failures, and strategies for reliable consumption. (kafka.apache.org)
Module 5: Kafka Storage, Replication, and Reliability
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Understanding Kafka's distributed log model, record retention, segment management, replication, leaders, followers, and fault tolerance.
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Exploring replication factors and partition replicas and understanding their role in maintaining data availability across broker failures.
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Applying retention and log-compaction concepts to support different event storage, replay, and state-management requirements.
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Designing reliable Kafka environments by considering durability, availability, recovery, replication, and operational requirements.
Module 6: Serialization, Delivery Semantics, and Data Quality
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Understanding serialization and deserialization and selecting appropriate approaches for structured and semi-structured event data.
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Exploring at-most-once, at-least-once, and exactly-once processing concepts and their implications for application design.
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Handling duplicate events, retries, ordering, idempotency, failed processing, and offset commits in reliable streaming applications.
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Applying appropriate data validation, schema management, error handling, and event-design practices to improve pipeline quality.
Module 7: Kafka Connect and Data Integration
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Understanding Kafka Connect architecture, workers, connectors, tasks, source connectors, and sink connectors for scalable data integration.
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Designing source integrations that bring information from databases, applications, files, and other systems into Kafka topics.
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Designing sink integrations that deliver Kafka events to databases, storage platforms, analytical systems, and downstream applications.
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Managing connector configuration, scalability, fault tolerance, monitoring, and operational considerations for enterprise integration workflows. (kafka.apache.org)
Module 8: Kafka Streams and Real-Time Data Processing
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Introduction to Kafka Streams and its role in developing applications that process and transform event streams directly within Kafka-based architectures.
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Applying stream transformations such as filtering, mapping, grouping, aggregation, joining, and repartitioning for analytical processing.
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Understanding stream-processing concepts including state stores, topology, tasks, application instances, and event-processing workflows.
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Designing practical Kafka Streams solutions for real-time enrichment, aggregation, monitoring, event detection, and streaming analytics. (kafka.apache.org)
Module 9: Kafka Security, Monitoring, and Performance
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Understanding Kafka security concepts including authentication, authorization, access control, encryption, and secure client connectivity.
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Monitoring brokers, producers, consumers, partitions, throughput, latency, consumer lag, and other operational indicators.
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Identifying performance bottlenecks involving partitions, network utilization, disk performance, producer settings, consumer behavior, and broker resources.
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Applying practical troubleshooting techniques to investigate connectivity failures, consumer issues, replication problems, processing delays, and cluster health concerns.
Module 10: End-to-End Kafka Data Streaming Solution
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Designing an end-to-end Kafka architecture that ingests events, distributes records, processes streams, and delivers information to downstream systems.
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Building a practical producer-to-consumer workflow incorporating topics, partitions, consumer groups, serialization, offsets, and reliability considerations.
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Integrating Kafka with external platforms using Kafka Connect and evaluating when Kafka Streams is appropriate for real-time event processing.
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Applying course concepts to realistic enterprise scenarios and developing scalable Kafka solutions for analytics, integration, and event-driven applications.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
