Description

This curriculum spans the technical and operational rigor of a multi-workshop architecture series, addressing the same depth of decision-making required in enterprise streaming platform rollouts, from schema governance and stateful processing to compliance-aligned security and cross-system observability.

Module 1: Architecting Real-Time Data Ingestion Pipelines

Design Kafka topic partitioning strategies based on event volume and consumer concurrency requirements
Select appropriate serialization formats (Avro vs. Protobuf) considering schema evolution and cross-service compatibility
Configure message retention policies balancing storage costs with replay needs for downstream systems
Implement idempotent producers to prevent duplicate message injection during network retries
Integrate TLS encryption and SASL authentication for secure data transmission across clusters
Deploy distributed log brokers with replication factor and ISR configurations to ensure fault tolerance
Size broker JVM heap and garbage collection parameters to avoid long pauses under sustained load
Monitor producer latency and broker request queue depth to detect early signs of backpressure

Module 2: Schema Design and Governance in Streaming Contexts

Enforce schema compatibility rules (backward, forward, full) in Schema Registry based on consumer upgrade cycles
Implement schema versioning workflows that align with CI/CD pipelines for data contracts
Define canonical event schemas for domain entities shared across business units
Resolve naming collisions in schema evolution by introducing namespace prefixes and version suffixes
Automate schema validation in pull requests using pre-commit hooks and linters
Document semantic meaning of fields using metadata annotations consumable by data discovery tools
Manage deprecation of fields with grace periods and consumer impact assessments
Enforce schema usage via policy-as-code in ingestion gateways

Module 3: Stream Processing with Stateful Workloads

Size state stores based on key cardinality and retention duration to prevent out-of-memory failures
Configure changelog topics for state backup with proper cleanup policies and replication
Implement punctuators for time-based state eviction in session window aggregations
Design state partitioning strategies to align with input topic partitioning and avoid shuffling
Handle tombstone events correctly to prevent state bloat from deleted keys
Test state recovery behavior after broker rebalances using controlled failover scenarios
Monitor state store read/write latency to detect performance degradation
Use interactive queries to expose state for operational debugging and auditing

Module 4: Event Time Processing and Temporal Consistency

Configure watermark generation policies based on observed event time skew in upstream systems
Select window types (tumbling, hopping, session) based on business SLAs and data arrival patterns
Handle late-arriving events using allowed lateness thresholds and side outputs for manual review
Implement watermark alignment across parallel sources to prevent straggler-induced stalls
Validate event time distribution using histogram metrics in monitoring dashboards
Reconcile processing time vs. event time for audit trails and compliance reporting
Design retry mechanisms that preserve event time ordering across reprocessing cycles
Use ingestion timestamps as fallback when event time is missing or invalid

Module 5: Data Quality and Anomaly Detection in Streams

Deploy schema conformance checks at ingestion points with quarantine topics for invalid events
Calculate real-time completeness and uniqueness metrics using sliding windows
Implement statistical anomaly detection on event rates using exponential moving averages
Flag outliers in numeric payloads using z-score thresholds updated in real time
Correlate data quality signals across related streams for root cause analysis
Route malformed events to dead-letter queues with contextual metadata for triage
Version data quality rules independently of processing logic for operational agility
Generate synthetic test events to validate detection logic under controlled conditions

Module 6: Integration with Batch and Lakehouse Systems

Design CDC pipelines from streaming topics to data lake partitions using transactional commits
Synchronize schema changes between streaming registry and data catalog in both directions
Implement compaction jobs to merge updates for slowly changing dimensions in the lake
Orchestrate backfill workflows that replay topics without disrupting real-time consumers
Use snapshot isolation when reading from changelog topics to ensure consistency
Configure partitioning strategies in Parquet/ORC files to optimize query performance
Propagate event time boundaries to batch processing windows for end-to-end consistency
Monitor end-to-end latency from source to lake using watermark tracking

Module 7: Security, Compliance, and Auditability

Implement field-level encryption for PII using envelope encryption and key rotation
Enforce role-based access control on topics and consumer groups via centralized policy engine
Generate audit logs for all data access and configuration changes with immutable storage
Support data subject right requests by enabling event traceability across transformations
Mask sensitive data in logs and monitoring tools using dynamic redaction rules
Conduct periodic access certification reviews for high-privilege streaming roles
Integrate with data lineage tools to map end-to-end flow from source to consumption
Validate regulatory compliance (e.g., GDPR, CCPA) through automated control checks

Module 8: Operational Resilience and Performance Tuning

Design disaster recovery strategies with cross-cluster mirroring and failover playbooks
Set up alerting on consumer lag exceeding business-defined SLAs
Conduct load testing using realistic event distributions and peak traffic patterns
Tune consumer fetch sizes and poll intervals to balance throughput and CPU usage
Implement circuit breakers in downstream integrations to prevent cascading failures
Use canary deployments for stream processing applications with traffic shadowing
Profile serialization/deserialization overhead in high-throughput pipelines
Optimize compaction and cleanup policies to reduce broker disk I/O pressure

Module 9: Monitoring, Observability, and Cost Management

Instrument end-to-end tracing across microservices using W3C Trace Context in events
Aggregate metrics from brokers, consumers, and processors in a unified time-series database
Define SLOs for processing latency and availability with error budget tracking
Tag resources by cost center and project for granular cloud billing allocation
Correlate infrastructure metrics with business KPIs to demonstrate value delivery
Implement log sampling strategies to control observability costs at scale
Use cardinality analysis to prevent metric explosion from high-dimension labels
Conduct quarterly cost reviews to decommission underutilized topics and consumers