The digital era is increasingly driven by data that flows continuously in real time, enabling organizations to make faster and more intelligent decisions. From financial transactions and e-commerce platforms to IoT devices and social media interactions, streaming data is reshaping how businesses operate and innovate. This course provides an in-depth exploration of streaming data engineering, with a strong emphasis on Apache Flink and Apache Kafka two of the most powerful open-source technologies for real-time data processing and analytics.
Participants will develop a solid understanding of the principles behind event-driven architectures and streaming-first ecosystems. They will learn how to design, build, and maintain resilient streaming pipelines capable of handling high-velocity data while ensuring low latency and high throughput. Through hands-on labs, case studies, and guided projects, learners will experience the end-to-end workflow of deploying and managing real-time systems in enterprise environments.
The course bridges theory with practical implementation, emphasizing distributed data processing, fault-tolerance mechanisms, stream processing frameworks, and integration strategies. Learners will explore not only Kafka and Flink but also their interaction with broader ecosystems including Spark, Kubernetes, and cloud-native services such as AWS Kinesis, Azure Event Hubs, and Google Pub/Sub.
As businesses increasingly shift toward real-time decision-making, the ability to harness streaming data has become a critical competitive advantage. This course ensures participants acquire the necessary expertise to design robust streaming architectures, apply transformations, and deploy analytics pipelines that power modern applications like fraud detection, personalization, IoT monitoring, and predictive analytics.
Beyond technical depth, the training covers essential aspects such as governance, monitoring, scaling, and compliance, ensuring that learners can deliver secure, compliant, and sustainable streaming ecosystems. By combining both strategic and operational perspectives, the course equips participants to meet the demands of real-world enterprise deployments.
By the end of the training, participants will be well-prepared to implement enterprise-ready real-time streaming data pipelines, unlocking the potential of continuous intelligence and setting the foundation for advanced analytics and AI-driven business solutions.
Who Should Attend
- Data Engineers seeking to specialize in real-time processing frameworks.
- Cloud and Solution Architects designing streaming-first ecosystems.
- DevOps Engineers managing distributed data platforms.
- Software Developers integrating event-driven systems into applications.
- Data Architects modernizing enterprise analytics infrastructure.
- IT Managers overseeing real-time data strategy and transformation.
- Machine Learning Engineers building feature pipelines for live predictions.
- IoT and Telecom engineers handling sensor and device data streams.
- Business Intelligence and Analytics professionals using live dashboards.
- Professionals preparing for Flink, Kafka, and real-time engineering certifications.
Duration
10 days
Course Objectives
By the end of this training, participants will be able to:
- Understand the architecture and fundamentals of streaming data systems.
- Design and implement real-time data pipelines using Flink and Kafka.
- Build scalable, fault-tolerant, and low-latency streaming ecosystems.
- Apply event-driven architectures for enterprise applications.
- Integrate streaming platforms with cloud-native services (AWS, Azure, GCP).
- Leverage Kafka topics, partitions, and consumer groups for high throughput.
- Master stateful stream processing with Apache Flink.
- Implement advanced features like windowing, watermarking, and joins in Flink.
- Enable real-time analytics, dashboards, and predictive modeling pipelines.
- Apply governance, compliance, and security practices in streaming workflows.
- Monitor, troubleshoot, and optimize streaming pipeline performance.
- Prepare for certification and real-world deployment of streaming platforms.
Comprehensive Course Outline
Module 1: Introduction to Streaming Data Engineering
- Fundamentals of batch vs. streaming data processing.
- Key use cases: fraud detection, IoT, recommendation engines.
- Core principles of event-driven architectures.
- Streaming ecosystem overview: Kafka, Flink, Spark Streaming.
Module 2: Apache Kafka Foundations
- Kafka architecture: brokers, topics, partitions, producers, consumers.
- Designing scalable Kafka clusters.
- Kafka Streams API and KSQL basics.
- Reliability and fault tolerance in Kafka.
Module 3: Apache Flink Foundations
- Flink runtime and architecture.
- Key abstractions: DataStream and DataSet APIs.
- Stateful vs. stateless stream processing.
- Hands-on: building a simple streaming pipeline in Flink.
Module 4: Event-Driven Architecture Patterns
- Publish-subscribe models in streaming systems.
- Designing microservices with Kafka and Flink.
- Event sourcing and CQRS patterns.
- Case study: enterprise-grade event-driven pipelines.
Module 5: Data Ingestion Strategies
- Real-time ingestion from IoT, sensors, and logs.
- Integration with cloud-native services (AWS Kinesis, Azure Event Hubs).
- CDC (Change Data Capture) with Debezium.
- Best practices for ingestion reliability and scaling.
Module 6: Data Transformation in Streaming Pipelines
- ETL vs. ELT in real-time contexts.
- Windowing and watermarking in Flink.
- Stream joins and aggregations.
- Handling schema evolution and data quality issues.
Module 7: Real-Time Analytics and Dashboards
- Connecting Kafka and Flink to BI tools.
- Real-time dashboards with Grafana and Kibana.
- Live anomaly detection and alerting.
- Use case: predictive analytics in real time.
Module 8: Deployment and Containerization
- Deploying Kafka and Flink on Kubernetes.
- Dockerizing streaming applications.
- CI/CD pipelines for streaming deployments.
- Cloud deployment strategies and hybrid setups.
Module 9: Reliability, Scalability, and Fault Tolerance
- Flink checkpointing and state management.
- Kafka replication and partitioning strategies.
- Elastic scaling of streaming pipelines.
- Disaster recovery strategies for real-time systems.
Module 10: Monitoring and Observability
- Metrics collection with Prometheus and Grafana.
- Log aggregation with ELK stack.
- Troubleshooting latency and throughput issues.
- Setting alerts and automated responses.
Module 11: Security and Compliance in Streaming Systems
- Authentication and authorization with Kafka (SASL, ACLs).
- Data encryption and secure communication channels.
- Compliance frameworks for real-time data (GDPR, HIPAA).
- Designing pipelines with audit trails and lineage.
Module 12: Advanced Flink Features
- Flink SQL for stream querying.
- Machine learning pipelines with Flink ML.
- CEP (Complex Event Processing) in Flink.
- Advanced operator tuning and optimization.
Module 13: Advanced Kafka Features
- Kafka Connect for integrating external systems.
- Stream processing with Kafka Streams API.
- Schema management with Confluent Schema Registry.
- Real-world Kafka cluster optimization.
Module 14: Multi-Cloud and Hybrid Streaming Pipelines
- Cross-cloud data streaming strategies.
- Kafka MirrorMaker and Confluent tools.
- Hybrid pipelines spanning on-premise and cloud.
- Governance challenges in multi-cloud streaming.
Module 15: Industry Case Studies
- Real-time fraud detection in financial services.
- IoT sensor streaming in manufacturing.
- Recommendation engines in e-commerce.
- Real-time user analytics in social platforms.
Module 16: Project and Certification Preparation
- End-to-end project: building a live streaming pipeline with Kafka + Flink.
- Peer-reviewed solution designs.
- Mock certification and interview preparation.
- Roadmap for continuous learning in streaming data engineering.
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 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 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.
