As data velocity and volume explode in today’s digital ecosystem, understanding the architectural trade-offs between batch and stream processing has become essential for building scalable, real-time, and cost-effective data systems. The Batch vs Stream Processing: Architecture and Trade-offs Training Course equips data engineers, architects, and developers with the tools to design, evaluate, and optimize both paradigms. Participants will gain deep insights into real-time vs historical data processing needs, architecture design, system latency, storage implications, use case applicability, and how to combine both approaches effectively for hybrid solutions using modern data tools.

Duration: 10 Days

Target Audience

• Data Engineers
• Cloud Architects
• Big Data Developers
• DevOps Engineers
• ETL/ELT Specialists
• Systems Integration Professionals
• Data Platform Engineers
• Software Architects

Course Objectives

• Understand core differences between batch and stream processing
• Analyze architectural models and trade-offs for latency, throughput, and consistency
• Design and deploy scalable batch and real-time data pipelines
• Learn about tools like Apache Spark, Kafka, Flink, and their use cases
• Explore fault tolerance and recovery mechanisms in streaming systems
• Optimize for cost, storage, and processing time across workloads
• Implement hybrid architectures combining batch and stream workflows
• Apply use-case-driven patterns for analytics, monitoring, and ingestion
• Understand processing guarantees and windowing strategies
• Evaluate performance using metrics and logging
• Ensure system reliability under dynamic data loads

Course Modules

Module 1: Introduction to Data Processing Paradigms

• Historical context of data processing evolution
• Real-time vs batch data: characteristics and examples
• Where batch and stream processing apply
• Emerging trends in unified architectures
• Business drivers for adopting each model

Module 2: Batch Processing Fundamentals

• Definition and characteristics of batch processing
• Scheduling, batching windows, and ETL cycles
• Common tools and frameworks: Spark, Hadoop
• Use cases: analytics, reporting, warehousing
• Data lake integration and bulk operations

Module 3: Stream Processing Fundamentals

• What defines a stream processing model
• Continuous ingestion and low-latency systems
• Key technologies: Apache Kafka, Flink, Spark Streaming
• Use cases: fraud detection, alerts, real-time dashboards
• Core concepts: event time, ingestion time, processing time

Module 4: Core Architecture Patterns

• Lambda Architecture: strengths and limitations
• Kappa Architecture for stream-only systems
• Micro-batch architecture and its role
• Data lakehouse integration with streaming
• Orchestration and dependency management

Module 5: Message Queuing and Brokers

• Overview of event brokers: Kafka, RabbitMQ, Pulsar
• Message formats and serialization: Avro, Protobuf
• Topic partitioning, retention policies, and offsets
• Producer-consumer models
• Broker reliability and scaling

Module 6: Stream Processing Internals

• Stateless vs stateful streaming
• Windowing: tumbling, sliding, session
• Watermarks and late data handling
• Processing guarantees: at-most-once, at-least-once, exactly-once
• Backpressure and load balancing

Module 7: Batch Architecture in Practice

• Data ingestion from multiple sources
• Batch pipeline orchestration with Airflow or Prefect
• Data transformations and aggregation at scale
• Job scheduling, retry mechanisms, and alerts
• Writing to data warehouses and lakes

Module 8: Real-Time Analytics and Event-Driven Systems

• Real-time dashboards with Kafka + ksqlDB
• Clickstream and IoT analytics pipelines
• Event-driven microservices with streaming backends
• User behavior modeling and alerts
• Integration with BI tools like Superset, Looker

Module 9: Hybrid Processing Models

• When to use batch and stream together
• Handling latency-sensitive and delayed data
• Combining Spark + Kafka for layered processing
• Slowly Changing Dimensions (SCD) in hybrid systems
• Pros and cons of unified APIs

Module 10: Fault Tolerance and Error Handling

• Checkpointing and state snapshots
• Replay mechanisms in streaming pipelines
• Error queues and dead letter topics
• Resilience patterns for processing pipelines
• Monitoring for processing anomalies

Module 11: Storage Strategies and Trade-offs

• Cold vs hot data storage layers
• Time-partitioned storage for batch
• Persistent vs ephemeral data in streaming
• Optimizing data formats for reads/writes
• Data retention, TTL, and legal compliance

Module 12: Cost Optimization and Performance

• Cost modeling for batch vs streaming
• Autoscaling and serverless considerations
• Throughput optimization for Kafka consumers
• Spark tuning and parallelism strategies
• Benchmarking tools and techniques

Module 13: Security and Compliance in Data Pipelines

• Authentication and authorization
• Data encryption at rest and in transit
• Masking and redacting sensitive data
• Compliance-aware data processing
• Security across batch and streaming frameworks

Module 14: Monitoring and Observability

• Metrics for pipeline health and performance
• Integrating with Prometheus, Grafana, or Datadog
• Structured logging and tracing pipelines
• Lag monitoring in Kafka and Flink
• Alerting on pipeline failure or slowdowns

Module 15: Designing for Scalability and Future-Proofing

• Sharding and partitioning best practices
• Scaling consumer groups and Spark executors
• Modularizing pipeline components
• Designing for cloud-native deployment
• Future trends: real-time ML, event mesh, edge streaming

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.