Stream Processing Systems - DataPipe Tokyo

Course Overview

This program covers Apache Kafka architecture including topic management, producer and consumer patterns, and cluster operations for reliable message delivery. Participants develop skills in designing event-driven systems that process continuous data streams with millisecond latencies.

Students learn Apache Flink for complex event processing, implementing windowing operations, stateful computations, and exactly-once semantics. The curriculum addresses backpressure handling, watermark strategies, and streaming SQL for declarative transformations on unbounded datasets.

Hands-on projects focus on building fraud detection systems, IoT data pipelines, and real-time recommendation engines. Labs include implementing event sourcing patterns, CQRS architectures, and integrating streaming platforms with data lakes for unified batch and streaming analytics.

Technical sessions cover Kafka Streams applications, schema evolution with Avro and Protobuf, and monitoring streaming topologies. Participants work with production scenarios involving partitioning strategies, consumer group rebalancing, and building fault-tolerant applications with state management.

Expected Outcomes

Real-Time Processing

Build streaming applications that process events with low latency using appropriate windowing and aggregation techniques

Event-Driven Architecture

Design scalable systems using event sourcing and CQRS patterns for maintaining consistency across distributed services

Reliable Delivery

Implement exactly-once processing semantics and handle failures gracefully with proper checkpointing and recovery mechanisms

Performance Tuning

Optimize streaming pipelines through proper partitioning, parallelism configuration, and resource allocation strategies

Technical Stack

Streaming Platforms

Apache Kafka ecosystem
Kafka Streams API
Apache Flink framework
Confluent Platform

Processing Patterns

Window operations
Stateful transformations
Event sourcing
CQRS implementation

Integration Layer

Schema registry
Kafka Connect
Stream-table joins
Sink connectors

Development Environment

Students work with containerized Kafka clusters using Docker Compose for local development and testing. The curriculum emphasizes infrastructure-as-code with deployment scripts, monitoring dashboards using Prometheus and Grafana, and observability patterns for distributed streaming systems.

Projects incorporate schema management with Confluent Schema Registry, data serialization formats including Avro and Protobuf, and integration testing frameworks. Participants gain experience with operational concerns including cluster sizing, replication configuration, and disaster recovery procedures.

Operational Standards

Reliability Practices

Course content covers establishing monitoring and alerting systems for streaming applications including lag metrics, throughput rates, and error tracking. Students learn to implement circuit breakers, retry policies, and dead letter queues for handling processing failures without data loss.

Health check endpoints
Checkpoint configuration

Replication strategies
Backpressure handling

Data Quality Measures

Training includes implementing validation logic within streaming pipelines to detect anomalies and schema violations. Students learn techniques for maintaining data accuracy during high-throughput processing including duplicate detection, late event handling, and watermark-based ordering guarantees.

Schema validation
Duplicate elimination

Event ordering
Late arrival handling

Who Should Attend

Ideal For

Engineers building real-time data applications
System architects designing event-driven platforms
Backend developers implementing messaging systems
Data engineers expanding into streaming analytics

Prerequisites

Programming skills in Java, Scala, or Python
Understanding of distributed systems concepts
Familiarity with API design and data formats
Basic knowledge of containerization with Docker

Skill Development Tracking

Technical Evaluations

Progress assessments through implementing streaming applications that meet specific latency and throughput requirements. Students receive detailed feedback on architecture decisions, code organization, and operational considerations. Weekly assignments build from simple producers and consumers to complex stateful applications.

7

Streaming assignments

3

Architecture projects

1

Production deployment

Application Metrics

Track development through measurable indicators including processing latency, throughput rates, and system reliability. Students benchmark their implementations against performance targets and receive guidance on optimization strategies. The curriculum emphasizes monitoring and observability for production systems.

End-to-end latency measurements
Throughput capacity testing
Fault recovery validation
Operational dashboard creation

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