The digital era has shifted the demand from batch processing to real-time data processing, enabling organizations to capture insights and make decisions instantly. Companies across industries rely on streaming technologies such as Apache Kafka and Apache Spark to handle high-velocity data, ensuring speed, scalability, and agility in critical business operations. This course provides a comprehensive foundation for professionals looking to master real-time data pipelines, event streaming, and in-memory analytics using industry-leading frameworks.
Participants will gain a deep understanding of how to design, build, and maintain real-time data architectures that power applications such as fraud detection, recommendation engines, IoT platforms, and operational dashboards. By combining Kafka’s distributed streaming capabilities with Spark’s powerful in-memory analytics engine, learners will develop skills to handle large-scale, fast-moving data streams with resilience and accuracy.
The course emphasizes practical learning through case studies, labs, and hands-on projects that reflect real-world scenarios. Learners will explore core concepts like Kafka producers and consumers, Spark streaming, data ingestion pipelines, schema management, and stream processing patterns. Beyond the basics, advanced topics such as fault tolerance, exactly-once semantics, and performance tuning will also be covered.
As organizations increasingly adopt data-driven strategies, professionals with expertise in real-time data processing are in high demand. This program is tailored to equip participants not only with technical mastery but also with the ability to align real-time processing solutions with business needs, ensuring speed, reliability, and scalability.
By the end of this training, participants will have the knowledge and hands-on expertise to design event-driven architectures, optimize streaming systems, and lead real-time data initiatives that provide a competitive edge in today’s fast-moving markets.
Who Should Attend
- Data engineers seeking to specialize in real-time data processing and streaming pipelines.
- Software developers building event-driven and streaming-based applications.
- System architects designing resilient, high-throughput data platforms.
- Data analysts and data scientists working with continuous streams of data.
- Database administrators expanding expertise to distributed, real-time environments.
- Cloud engineers and DevOps professionals managing data infrastructure at scale.
- IT managers overseeing analytics modernization and real-time decision systems.
- Consultants supporting enterprise adoption of real-time technologies.
- Business intelligence professionals integrating real-time analytics into dashboards.
- Executives and leaders seeking to understand the value of real-time insights.
Duration
5 days
Course Objectives
By completing this course, participants will be able to:
- Understand the fundamentals of real-time data processing and streaming architectures.
- Build, configure, and manage Apache Kafka clusters for scalability and resilience.
- Use Apache Spark for real-time analytics, stream processing, and machine learning integration.
- Design end-to-end real-time data pipelines for high-velocity workloads.
- Apply data ingestion, serialization, and schema management best practices.
- Optimize performance and ensure fault tolerance in streaming systems.
- Implement exactly-once semantics and reliable message delivery.
- Integrate Kafka and Spark with cloud platforms and BI tools.
- Manage governance, monitoring, and security for real-time pipelines.
- Apply real-world use cases in finance, healthcare, IoT, and e-commerce.
Comprehensive Course Outline
Module 1: Introduction to Real-Time Data Processing
- Batch vs. real-time vs. near-real-time paradigms.
- Business drivers and industry applications of real-time processing.
- Event-driven architectures and microservices integration.
- Key challenges and opportunities in real-time analytics.
Module 2: Apache Kafka Foundations
- Kafka architecture: brokers, topics, partitions, and offsets.
- Producers, consumers, and consumer groups explained.
- Message serialization (Avro, JSON, Protobuf).
- Kafka use cases: log aggregation, stream processing, and messaging systems.
Module 3: Kafka Deployment and Management
- Installing and configuring Kafka clusters.
- Zookeeper and Kafka cluster coordination.
- High availability, replication, and fault tolerance.
- Monitoring Kafka with tools like Prometheus and Grafana.
Module 4: Apache Spark Fundamentals
- Spark architecture: RDDs, DataFrames, and DAG execution.
- Spark Streaming and Structured Streaming basics.
- Windowing, triggers, and late event handling.
- Spark use cases for real-time analytics and machine learning.
Module 5: Building Real-Time Pipelines
- Designing ingestion pipelines with Kafka and Spark.
- Stream joins, aggregations, and transformations.
- Handling unbounded data streams efficiently.
- End-to-end pipeline orchestration with Airflow and Flink.
Module 6: Performance Optimization
- Tuning Kafka producers, consumers, and brokers.
- Spark performance optimization strategies.
- Memory management and cluster resource allocation.
- Benchmarking throughput and latency for pipelines.
Module 7: Fault Tolerance and Reliability
- Ensuring data durability and exactly-once semantics.
- Handling retries, dead-letter queues, and error recovery.
- Implementing checkpointing in Spark Streaming.
- Designing disaster recovery and failover strategies.
Module 8: Integration with Cloud and BI Tools
- Running Kafka and Spark on AWS, Azure, and GCP.
- Using managed services like AWS MSK and Dataproc.
- Integration with BI tools like Tableau and Power BI.
- Hybrid and multi-cloud strategies for real-time workloads.
Module 9: Governance, Security, and Compliance
- Authentication and authorization in Kafka and Spark.
- Role-based access control and encryption.
- Audit logging and monitoring for compliance.
- Meeting industry standards (GDPR, HIPAA, PCI DSS).
Module 10: Advanced Use Cases and Future Trends
- Real-time fraud detection and anomaly detection.
- IoT data streams and sensor data processing.
- Real-time personalization and recommendation systems.
- Emerging trends: serverless streaming, AI-driven pipelines, and edge computing.
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
