A tailored course, built for your situation
Mastering Event Stream Processing at Scale
A 12-module deep-dive for engineers turning real-time data into decisive action
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
You're expected to deliver systems that react instantly , but without blowing up under load or breaking in production.
The situation this course is for
Event stream processing is moving from experimental to essential. Yet most engineers learn through fragmented documentation or trial-by-fire. The gap between knowing the tools and knowing the patterns leads to overcomplicated pipelines, debugging nightmares, and systems that fail when scaled. The pressure to deliver real-time results is rising , but the path to mastery remains unclear.
Who this is for
Senior software engineers, data engineers, and platform architects implementing or evolving event-driven systems in production.
Who this is not for
This is not for beginners learning SQL or basic data pipelines. It’s not for managers seeking high-level overviews. It’s not for teams using batch-only workflows with no plans to adopt streaming.
What you walk away with
- Architect resilient, low-latency stream processing pipelines
- Choose the right tools and patterns for your scale and SLA
- Avoid common failure modes in stateful processing and checkpointing
- Optimize for fault tolerance, reprocessing, and data consistency
- Lead stream processing adoption with confidence and clarity
The 12 modules (with all 144 chapters)
Module 1. Foundations of Event-Driven Systems
Establish core concepts including events vs messages, event sourcing, and the role of time in distributed systems. Understand how modern architectures shift from request-response to event-first thinking.
12 chapters in this module
- What is an event?
- Event vs message
- Event sourcing basics
- Time in distributed systems
- Event metadata design
- Schema evolution
- Event identity
- Event versioning
- Event partitioning
- Event routing
- Event store types
- Event delivery semantics
Module 2. Streaming Platforms Overview
Compare Kafka, Pulsar, Flink, and SQS in terms of throughput, durability, and operational complexity. Learn where each excels and how to evaluate trade-offs.
12 chapters in this module
- Kafka architecture
- Pulsar advantages
- Flink processing model
- SQS limitations
- Pub/sub systems
- Message queuing
- Log-based systems
- Streaming durability
- Partition strategies
- Replication settings
- Scaling brokers
- Latency benchmarks
Module 3. Event Ingestion Patterns
Design reliable ingestion from databases, APIs, and user actions. Learn CDC, batching, backpressure, and schema validation at the edge.
12 chapters in this module
- Change Data Capture
- Database binlog
- API to stream
- User event capture
- Ingestion buffering
- Backpressure handling
- Schema validation
- Field normalization
- Error ingestion
- Retry strategies
- Ingestion monitoring
- Rate limiting
Module 4. State Management in Streams
Understand how stateful processing works in Flink, Kafka Streams, and custom systems. Learn checkpointing, state stores, and recovery techniques.
12 chapters in this module
- Stateful functions
- Keyed state
- Operator state
- Checkpointing
- Savepoints
- State backends
- RocksDB tuning
- Memory management
- State size
- State cleanup
- State migration
- State consistency
Module 5. Time and Windowing
Master event time, processing time, watermarks, and window types. Learn how to reason about time in unreliable networks.
12 chapters in this module
- Event time
- Processing time
- Watermarks
- Tumbling windows
- Sliding windows
- Session windows
- Late data
- Window triggers
- Window accumulation
- Time zones
- Clock drift
- Time synchronization
Module 6. Processing Guarantees
Achieve exactly-once, at-least-once, and at-most-once semantics. Understand trade-offs between correctness and performance.
12 chapters in this module
- Idempotency
- Transactional writes
- Two-phase commit
- 幂等处理
- Duplicate handling
- Ack patterns
- Offset management
- Delivery semantics
- End-to-end consistency
- Source guarantees
- Sink guarantees
- Error recovery
Module 7. Scaling and Parallelism
Design systems that scale horizontally. Learn partitioning, rebalancing, and load distribution strategies for high-throughput streams.
12 chapters in this module
- Partition keys
- Rebalancing
- Consumer groups
- Parallel operators
- Scaling sources
- Scaling sinks
- Load balancing
- Hot partitions
- Skew handling
- Dynamic scaling
- Resource allocation
- Concurrency tuning
Module 8. Fault Tolerance and Recovery
Build systems that survive failures. Learn replication, checkpointing, and automated recovery patterns for zero data loss.
12 chapters in this module
- Broker failure
- Broker recovery
- Consumer failure
- Producer retry
- Replication factor
- ISR management
- Failover strategies
- Recovery time
- Data loss prevention
- Log compaction
- Retention policies
- Disaster recovery
Module 9. Monitoring and Observability
Instrument pipelines for visibility. Learn key metrics, alerting thresholds, and debugging strategies for complex stream topologies.
12 chapters in this module
- Lag monitoring
- Throughput tracking
- Error rates
- Latency percentiles
- Dead letter queues
- Tracing events
- Log aggregation
- Alerting rules
- Pipeline health
- Resource usage
- Garbage collection
- Thread dumps
Module 10. Security and Compliance
Secure data in motion and at rest. Implement auth, encryption, and audit trails for regulated environments.
12 chapters in this module
- TLS encryption
- SASL auth
- Role-based access
- Audit logging
- Data masking
- PII handling
- Encryption keys
- Secret management
- Network isolation
- Compliance logging
- Retention rules
- Access reviews
Module 11. Integration with Downstream Systems
Connect streams to databases, data lakes, and APIs reliably. Learn change propagation and consistency patterns.
12 chapters in this module
- Database sinks
- Kafka Connect
- S3 integration
- Elasticsearch index
- API endpoints
- Change propagation
- Consistency models
- Idempotent writes
- Retry backoff
- Fan-out patterns
- Event fan-in
- Cross-system tx
Module 12. Leading Stream Architecture Adoption
Drive organizational change by demonstrating value, reducing risk, and building internal expertise.
12 chapters in this module
- Pilot projects
- Use case selection
- Stakeholder alignment
- Team training
- Architecture review
- Risk assessment
- Scaling roadmap
- Tooling evaluation
- Cost modeling
- Performance goals
- Success metrics
- Knowledge transfer
How this maps to your situation
- You're designing a new event-driven system
- You're debugging a flaky streaming pipeline
- You're scaling beyond proof-of-concept
- You're advising others on stream architecture
Before vs. after
Before
Overwhelmed by fragmented tools, inconsistent patterns, and production fires in stream processing.
After
Confidently designing, debugging, and leading resilient, scalable event-driven systems.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 60, 70 hours of focused learning, designed to be completed alongside real-world projects.
If nothing changes
Without structured knowledge, teams default to fragile implementations , leading to outages, rework, and missed opportunities in real-time systems.
How this compares to the alternatives
Unlike generic tutorials or vendor-specific docs, this course focuses on transferable patterns, trade-offs, and implementation clarity , not just API usage.
Frequently asked
Who is this course for?
Engineers and architects building or evolving production-grade event stream processing systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there video content?
No, the course is entirely text-based with diagrams, templates, and implementation examples.
$199 one-time. Approximately 60, 70 hours of focused learning, designed to be completed alongside real-world projects..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours