Introduction

In the modern digital economy, the ability to process and react to data in real time is a critical differentiator, making Streaming Data Processing with Apache Kafka an indispensable skill for building responsive, scalable, and event-driven applications and data pipelines. Apache Kafka is the industry-standard distributed streaming platform, designed to handle high-velocity data streams with exceptional reliability and throughput, serving as a central nervous system for modern data architectures. This comprehensive training course is meticulously designed to equip data engineers, software developers, DevOps engineers, and data architects with cutting-edge knowledge and practical skills in mastering Kafka's core concepts, building robust data producers and consumers, exploring advanced features like Kafka Connect and Kafka Streams, and developing proficiency in deploying, managing, and securing Kafka clusters in a production environment. Participants will gain a deep understanding of how to architect and implement the real-time data infrastructure that is the lifeblood of today's most innovative companies.

Duration

10 days

Target Audience

• Data Engineers
• Software Developers
• DevOps Engineers
• Data Architects
• IT Professionals
• Data Scientists
• Cloud Engineers
• BI Developers
• Systems Administrators
• Anyone involved in real-time data processing

Objectives

• Understand the core concepts of streaming data and the role of Apache Kafka.
• Master Kafka's architecture, including brokers, topics, partitions, and zookeepers.
• Learn to build and configure Kafka producers for efficient data ingestion.
• Develop proficiency in building and consuming data from Kafka topics.
• Understand advanced features like Kafka Connect for integrating with other systems.
• Explore Kafka Streams for building stream-processing applications.
• Learn about Kafka administration, monitoring, and security best practices.
• Develop skills in building a complete, end-to-end streaming data pipeline.
• Understand the importance of fault tolerance and high availability in a Kafka cluster.
• Formulate a strategic approach to using Kafka for real-time analytics.

Course Content

Module 1. Introduction to Streaming Data and Kafka

• What is Streaming Data?: Its characteristics and importance
• Why Apache Kafka?: Its purpose, history, and key advantages
• The role of Kafka in a modern data ecosystem
• Use cases for real-time data processing
• The conceptual difference between batch and stream processing

Module 2. Kafka Core Concepts

• Topics and Partitions: The fundamental data structure in Kafka
• Producers and Consumers: The key actors in the Kafka ecosystem
• Brokers: The servers that run Kafka
• Offsets and Consumer Groups
• Log compaction and retention policies

Module 3. Kafka Architecture

• The Kafka Cluster: How brokers work together
• Zookeeper's Role: Managing metadata and coordinating brokers
• The internals of message publishing and consumption
• High availability and fault tolerance
• Understanding replication factor and leader election

Module 4. Kafka Producers

• Building a Producer: Writing a simple producer in Python/Java
• Producer Configuration: Acknowledgment levels, batching, compression
• Serialization and Deserialization
• Partitioning Strategies: Default, custom, and key-based
• Handling producer errors and retries

Module 5. Kafka Consumers

• Building a Consumer: Writing a simple consumer in Python/Java
• Consumer Configuration: group.id, offset management
• Consumer Groups and parallel consumption
• Consumer rebalancing and its impact
• Handling consumer errors and failures

Module 6. Building a Simple Data Pipeline

• End-to-End Project: Ingesting, processing, and storing data
• Step 1: Producer: Creating a script to generate data
• Step 2: Broker: Running a Kafka cluster
• Step 3: Consumer: Creating a consumer to process the data
• Connecting the components into a complete pipeline
• Using command-line tools for verification

Module 7. Advanced Producer and Consumer Settings

• Transactional Producers: Ensuring atomicity
• Idempotent Producers: Preventing duplicate messages
• Consumer Lag: Monitoring how far behind a consumer is
• Advanced offset management and exactly-once semantics (conceptual)
• Performance tuning for producers and consumers

Module 8. Kafka Connect

• What is Kafka Connect?: Its purpose and architecture
• Source Connectors: Ingesting data from external systems (e.g., databases, S3)
• Sink Connectors: Loading data into external systems (e.g., data warehouses)
• Configuring and managing connectors
• The standalone vs. distributed mode

Module 9. Kafka Streams

• What are Kafka Streams?: A client library for building stream applications
• Core Concepts: KStream, KTable, KGlobalTable
• State management in Kafka Streams
• Processing events: filter, map, join, aggregate
• Building a simple stream-processing application

Module 10. KSQL/K-SQLDB

• Introduction to KSQL: A SQL-like language for streaming
• Stream and Table DDL: Creating and managing streams and tables
• Using SELECT queries on streams and tables
• Building real-time materialized views and aggregations
• The benefits of KSQL for analysts

Module 11. Kafka Administration and Monitoring

• Broker Administration: Starting, stopping, rebalancing
• Topic Administration: Creating, deleting, altering topics
• Monitoring Kafka: JMX metrics, using tools like Prometheus/Grafana
• Managing partitions and replicas
• Common administrative tasks

Module 12. Security in Kafka

• Authentication: Securing communication with brokers
• Authorization: Access control using ACLs (Access Control Lists)
• Encryption: TLS/SSL for securing data in transit
• Data encryption at rest
• Best practices for securing a Kafka deployment

Module 13. Integration with Spark and Flink

• Spark Structured Streaming: Processing Kafka data with Spark
• Apache Flink: An alternative stream processing engine
• Connecting Spark and Flink to Kafka
• Building a Lambda or Kappa architecture
• Choosing the right stream processor for your needs

Module 14. Real-World Use Cases and Best Practices

• Real-time Analytics: Building live dashboards
• Event Sourcing: Using Kafka as a log of all events
• Microservices Communication: Using Kafka for service-to-service messaging
• Common architectural patterns with Kafka
• Best practices for designing and maintaining Kafka pipelines

Module 15. The Kafka Ecosystem and Future Trends

• The Confluent Platform: An enterprise Kafka solution
• The evolution of Kafka: New features and releases
• The role of Kafka in IoT and event-driven architectures
• Future trends in streaming data processing
• The Kafka community and resources for continued learning.

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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute 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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.