Introduction

In today's hyper-connected and rapidly evolving digital landscape, businesses can no longer afford to wait for batch processing to derive insights; the ability to analyze data as it is generated, in motion, is becoming a critical differentiator for competitive advantage, enabling immediate decision-making, proactive responses, and enhanced operational efficiency. Real-Time BI and Stream Analytics empowers organizations to process, monitor, and visualize continuous streams of data from diverse sources, transforming raw events into actionable intelligence within milliseconds or seconds, from fraud detection to personalized customer experiences and IoT monitoring. This training course is meticulously designed to equip data engineers, BI developers, data scientists, solution architects, operations managers, and IT professionals with cutting-edge knowledge and practical skills in understanding the principles of stream processing, mastering real-time data ingestion and transformation, leveraging leading stream analytics platforms (e.g., Apache Kafka, Apache Flink, Spark Streaming), designing real-time dashboards, implementing complex event processing (CEP), and addressing the challenges of latency, scalability, and fault tolerance in streaming architectures. Participants will gain a comprehensive understanding of how to build, deploy, and manage robust real-time BI solutions that deliver instant insights and drive immediate business value.

Duration

10 days

Target Audience

• Data Engineers
• BI Developers
• Data Scientists
• Solution Architects
• Operations Managers
• IT Professionals (Infrastructure & Data)
• Developers working with real-time systems
• Business Analysts (interested in real-time insights)
• IoT Engineers
• Risk Management Professionals

Objectives

• Understand the fundamental concepts and benefits of Real-Time BI and Stream Analytics.
• Master various techniques for real-time data ingestion and processing.
• Learn about leading stream processing frameworks (e.g., Apache Kafka, Flink, Spark Streaming).
• Develop proficiency in designing real-time data pipelines and architectures.
• Understand Complex Event Processing (CEP) and its applications.
• Explore real-time data visualization and dashboarding techniques.
• Learn about challenges in stream analytics: latency, throughput, fault tolerance.
• Develop skills in monitoring and optimizing real-time BI solutions.
• Understand the use cases and business value of instant insights.
• Formulate strategies for implementing and scaling real-time BI initiatives.
• Apply stream analytics to solve real-world business problems requiring immediate action.

Course Content

Module 1. Introduction to Real-Time BI and Stream Analytics

• Defining Real-Time BI: Beyond traditional batch processing
• What is Stream Analytics?: Processing data in motion
• Key characteristics of streaming data: Volume, Velocity, Variety, Veracity
• Business drivers for real-time insights: Competitive advantage, operational efficiency, customer experience
• Real-world examples of real-time BI applications (e.g., fraud detection, IoT monitoring)

Module 2. Fundamentals of Stream Processing

• Event-Driven Architecture: Concepts and components
• Stream vs. Batch Processing: Understanding the fundamental differences
• Event Time vs. Processing Time
• Windowing Techniques: Tumbling, Sliding, Session, Global windows
• State Management in stream processing

Module 3. Real-Time Data Ingestion with Apache Kafka

• Introduction to Apache Kafka: Distributed streaming platform
• Kafka Concepts: Producers, Consumers, Topics, Partitions, Brokers, ZooKeeper
• Setting up a Kafka cluster (conceptual)
• Producing and consuming messages from Kafka topics
• Kafka Connect for integrating with other systems

Module 4. Stream Processing with Apache Flink

• Introduction to Apache Flink: Unified stream and batch processing engine
• Flink Architecture: JobManager, TaskManagers, Dataflow programming model
• DataStream API: Transformations (map, filter, keyBy, window) and Sinks
• Stateful Stream Processing in Flink
• Fault tolerance and checkpointing in Flink

Module 5. Stream Processing with Apache Spark Streaming

• Introduction to Spark Streaming: Micro-batch processing
• DStreams (Discretized Streams): Creating, transforming, and outputting
• Integrating Spark Streaming with Kafka and other sources
• Fault tolerance and exactly-once semantics in Spark Streaming
• Comparison of Spark Streaming with Flink for real-time use cases

Module 6. Structured Streaming with Apache Spark

• Introduction to Structured Streaming: Leveraging DataFrames/Datasets for streams
• Continuous processing vs. Micro-batch processing in Structured Streaming
• Reading from streaming sources: Kafka, files, sockets
• Writing to streaming sinks: Console, memory, Kafka, files
• State management in Structured Streaming

Module 7. Complex Event Processing (CEP)

• Defining Complex Events: Patterns, sequences, and relationships of simple events
• CEP Engines: Principles and functionalities
• Use cases for CEP: Fraud detection, real-time alerting, predictive maintenance
• Designing event patterns and rules
• Integrating CEP with stream processing frameworks

Module 8. Real-Time Data Storage and Databases

• NoSQL Databases for Real-Time Data: Apache Cassandra, MongoDB, Redis
• Time-Series Databases: InfluxDB, TimescaleDB
• In-Memory Databases: SAP HANA, Apache Ignite
• Data storage considerations for high-velocity, high-volume data
• Fast data access patterns for real-time analytics

Module 9. Real-Time Data Visualization and Dashboarding

• Real-Time Dashboard Design Principles: Low latency, high refresh rates
• Streaming Visualizations: Live charts, gauges, alerts
• Using BI tools with real-time connectors (e.g., Power BI DirectQuery, Tableau Live)
• Web-based visualization libraries for streaming data (e.g., D3.js, Plotly)
• Designing for immediate action and operational awareness

Module 10. Real-Time Analytics Use Cases and Business Value

• Fraud Detection and Security Monitoring: Identifying suspicious activities instantly
• IoT and Sensor Data Analytics: Real-time asset monitoring, predictive maintenance
• Personalized Customer Experiences: Real-time recommendations, dynamic pricing
• Financial Trading and Market Data Analysis
• Operational Intelligence: Monitoring manufacturing lines, logistics

Module 11. Architecture for Real-Time BI Solutions

• Lambda Architecture: Batch layer, speed layer, serving layer
• Kappa Architecture: Stream-first approach
• Components of a typical real-time BI architecture
• Data flow and integration patterns
• Designing for scalability and resilience

Module 12. Challenges in Stream Analytics

• Latency Management: Minimizing delays in data processing
• Throughput and Scalability: Handling high volumes of data
• Fault Tolerance and Data Consistency: Ensuring reliability
• Data Quality in Streaming Data
• Data Governance for real-time data

Module 13. Monitoring and Optimization of Real-Time BI Systems

• Monitoring Key Metrics: Latency, throughput, error rates, resource utilization
• Alerting and Notification Systems
• Troubleshooting common issues in streaming pipelines
• Performance tuning of stream processing applications
• Capacity planning for real-time infrastructure

Module 14. Cloud-Based Real-Time Analytics Services

• AWS Streaming Services: Kinesis, Lambda, S3, Redshift
• Azure Streaming Services: Event Hubs, Stream Analytics, Cosmos DB
• Google Cloud Streaming Services: Pub/Sub, Dataflow, BigQuery
• Serverless stream processing
• Cost implications and managed services in the cloud

Module 15. Future Trends in Real-Time BI and Stream Analytics

• Edge Computing and Fog Computing: Processing data closer to the source
• AI and Machine Learning on Streams: Real-time anomaly detection, predictive models
• Complex Event Processing (CEP) Advancements: More sophisticated pattern recognition
• Data Mesh and Data Fabric for distributed real-time data
• The convergence of Operational Technology (OT) and Information Technology (IT) for real-time insights.

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.