In the fast-paced world of data, mastering Real-Time Data Processing with Flink and Kafka Streams is a transformative skill for organizations seeking to derive immediate insights from continuous data streams, enabling them to react to events as they happen and gain a significant competitive advantage. The convergence of Apache Kafka for high-throughput messaging and Apache Flink for powerful stream processing provides a robust, scalable, and fault-tolerant architecture for building mission-critical, real-time applications across various industries, from finance to IoT. This comprehensive training course is meticulously designed to equip data engineers, software developers, and data scientists with the advanced knowledge and practical strategies required to design, develop, and deploy production-grade stream processing pipelines using these leading technologies. Without robust expertise in Real-Time Data Processing with Flink and Kafka Streams, organizations risk latency in their data analysis, outdated business intelligence, and a failure to capitalize on the valuable, time-sensitive insights hidden within their data, underscoring the vital need for specialized expertise in this critical domain.

Duration: 10 Days

Target Audience

• Data Engineers and Architects
• Software Developers and DevOps Engineers
• Data Scientists working with streaming data
• Systems Administrators responsible for data infrastructure
• Big Data and Analytics Professionals
• Technical Leaders and Managers
• Anyone involved in designing or implementing real-time data solutions.

Objectives

• Understand the core concepts and architecture of real-time data processing.
• Learn the fundamental principles and components of Apache Kafka.
• Acquire skills in developing stream processing applications using Kafka Streams.
• Comprehend the architecture and capabilities of Apache Flink.
• Explore strategies for building robust data pipelines with Flink's DataStream API.
• Understand the critical role of state management and fault tolerance in streaming.
• Gain insights into advanced windowing, watermarking, and time concepts in Flink.
• Develop a practical understanding of joining and aggregating data streams.
• Master the use of Flink's Table API and SQL for streaming analytics.
• Acquire skills in deploying, monitoring, and managing Flink and Kafka Streams applications.
• Learn to apply best practices for building production-ready, scalable streaming solutions.
• Comprehend techniques for integrating Flink with other data sources and sinks.
• Explore strategies for ensuring data quality and consistency in real-time pipelines.
• Understand the importance of performance tuning for both Kafka and Flink.
• Develop the ability to lead and implement a successful Real-Time Data Processing with Flink and Kafka Streams project.

Course Content

Module 1: Introduction to Real-Time Data Processing

• The evolution of data processing: from batch to real-time.
• Key concepts: event-driven architecture, streaming data, and event time.
• Use cases for real-time analytics and stream processing.
• Overview of the modern streaming ecosystem.
• Choosing between different streaming technologies.

Module 2: Apache Kafka Fundamentals

• Kafka's architecture: topics, partitions, producers, and consumers.
• Setting up a Kafka cluster and Zookeeper.
• The role of Kafka as a central nervous system for data.
• Using Kafka command-line tools for topic management.
• Kafka's performance characteristics and durability.

Module 3: Introduction to Kafka Streams

• What is Kafka Streams and its place in the Kafka ecosystem?
• The Streams DSL (Domain-Specific Language).
• KStream and KTable abstractions and their differences.
• Building a simple stream processing application.
• Deploying and running a Kafka Streams application.

Module 4: Advanced Kafka Streams Concepts

• State stores and local state management.
• Windowing: hopping, tumbling, and session windows.
• Stream-to-stream and stream-to-table joins.
• Handling late-arriving data.
• Processor API for fine-grained control.

Module 5: Apache Flink Core Concepts

• Flink's architecture: JobManager, TaskManagers, and slots.
• The DataStream API vs. the DataSet API.
• Flink's programming model: sources, transformations, and sinks.
• Key differentiators of Flink: state, time, and fault tolerance.
• Setting up a Flink development environment.

Module 6: Building Stream Applications with Flink's DataStream API

• Flink sources: reading from Kafka, files, and sockets.
• Common transformations: map, filter, flatMap, and keyBy.
• Implementing aggregations and reductions.
• Flink sinks: writing to Kafka, databases, and files.
• Writing and submitting a Flink job.

Module 7: Advanced Time and Windowing in Flink

• Understanding processing time, event time, and ingestion time.
• Watermarks and their role in handling out-of-order events.
• Types of windows: tumbling, sliding, and session windows.
• Triggers and evictors for advanced windowing control.
• Implementing a real-world windowing scenario.

Module 8: Flink State Management and Fault Tolerance

• Managed state vs. raw state.
• Working with keyed state and operator state.
• Checkpointing and state backend configuration.
• Savepoints for versioning and upgrades.
• Ensuring exactly-once state consistency.

Module 9: Joins and Connects in Flink

• Stream-to-stream joins with windows.
• Stream-to-table joins with an external data source.
• Connecting two different data streams.
• Patterns for enriching a data stream with a static dataset.
• Best practices for designing join logic.

Module 10: Flink's Table API and SQL

• Introduction to the Table API and Flink SQL.
• Integrating with the DataStream API.
• Using Flink SQL for declarative stream processing.
• Connecting to various data catalogs and sources.
• Building a real-time dashboard using Flink SQL.

Module 11: Productionizing Kafka Streams Applications

• Packaging and deploying Kafka Streams jobs.
• Monitoring Kafka Streams applications.
• Managing and scaling Kafka Streams instances.
• Configuration best practices for production.
• Strategies for rolling upgrades and application health checks.

Module 12: Productionizing Flink Applications

• Flink deployment modes: standalone, YARN, Kubernetes.
• Monitoring Flink jobs using the Flink UI.
• High availability configurations for Flink clusters.
• Setting up logging and metrics.
• CI/CD pipelines for Flink projects.

Module 13: The Flink and Kafka Integration

• The Flink Kafka connector: architecture and configuration.
• Best practices for building end-to-end pipelines.
• Understanding data format compatibility.
• Using both Flink and Kafka Streams in a single ecosystem.
• Performance tuning the Flink-Kafka connection.

Module 14: Advanced Real-Time Concepts

• Handling complex event processing (CEP) with Flink.
• Introduction to the Flink ML and Graph APIs.
• Stream processing with Python (PyFlink).
• Integrating with other cloud services and data platforms.
• The future of real-time data processing.

Module 15: Practical Workshop: Building a Real-Time Pipeline

• Participants work in teams to design a complete streaming pipeline.
• Exercise: ingest data from a simulated source, process with Flink, and write to a dashboard.
• Implement windowing, state management, and fault tolerance.
• Deploy and monitor the application on a cluster.
• Present the final project and discuss design choices.

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.