This curriculum spans the technical and operational complexity of a multi-workshop program to build and govern event-driven systems, comparable to an internal capability initiative for real-time decisioning across customer operations, data engineering, and compliance functions.
Module 1: Defining Real-Time Use Cases in Customer Operations
- Selecting customer journey stages (e.g., onboarding, support escalation) where sub-second analytics impact conversion or retention
- Evaluating whether batch processing suffices versus true real-time for loyalty reward eligibility checks
- Mapping SLAs for data freshness across customer-facing teams (e.g., contact center vs. marketing)
- Identifying high-impact events such as cart abandonment or service degradation for real-time detection
- Aligning real-time KPIs (e.g., response latency to customer behavior) with business unit OKRs
- Conducting cost-benefit analysis of real-time interventions versus rule-based batch campaigns
- Documenting edge cases where real-time signals may misrepresent customer intent (e.g., accidental clicks)
- Negotiating data ownership between CRM, analytics, and digital product teams for event ingestion
Module 2: Architecting Event-Driven Data Pipelines
- Choosing between Kafka, Pulsar, or AWS Kinesis based on regional compliance and replication requirements
- Designing event schema versioning strategies to maintain backward compatibility during customer data model changes
- Implementing idempotent consumers to handle duplicate events from payment or clickstream sources
- Partitioning event streams by customer ID to ensure ordered processing within individual journeys
- Configuring dead-letter queues for malformed customer interaction events with automated alerting
- Integrating schema registries to enforce contract compliance across microservices publishing customer events
- Optimizing message size by compressing session payloads without losing diagnostic traceability
- Establishing network peering and encryption standards for cross-account event transmission
Module 3: Streaming Data Processing with Flink and Spark
- Configuring state backends in Apache Flink for fault-tolerant sessionization of customer touchpoints
- Choosing between event-time and processing-time windows for calculating real-time NPS from support interactions
- Managing watermark delays to balance accuracy and latency in customer lifetime value streaming models
- Scaling parallelism in Spark Structured Streaming based on peak-hour traffic from digital channels
- Implementing incremental aggregation to update customer engagement scores without full recomputation
- Handling late-arriving data from mobile apps with inconsistent connectivity using allowed lateness policies
- Monitoring checkpoint durations in Flink to prevent backpressure during flash sale events
- Securing access to streaming state stores containing PII in shared cluster environments
Module 4: Real-Time Feature Engineering for Decision Systems
- Building low-latency features such as rolling session duration or recent support ticket frequency
- Synchronizing feature store timestamps with model inference clocks to prevent data leakage
- Implementing TTL policies for cached customer behavior vectors to ensure recency
- Validating feature consistency across batch and streaming pipelines for A/B test integrity
- Versioning feature definitions when refining churn risk indicators based on new data sources
- Choosing between online and offline feature stores based on SLA requirements for recommendation engines
- Quantifying drift in real-time feature distributions during product launches or outages
- Encrypting sensitive derived features (e.g., spending velocity) in memory and transit
Module 5: Deploying Real-Time Machine Learning Models
- Containerizing models with GPU support for low-latency inference on customer image uploads
- Implementing canary rollouts for next-best-action models to isolate impact on conversion rates
- Designing fallback strategies when real-time scoring services exceed 200ms latency thresholds
- Embedding model metadata (version, input schema) in prediction responses for auditability
- Monitoring prediction skew across customer segments to detect training-serving discrepancies
- Integrating explainability payloads into real-time API responses for agent-assist tools
- Rotating model weights automatically based on offline performance decay metrics
- Enforcing model access controls using OAuth scopes aligned with customer data permissions
Module 6: Operationalizing Real-Time Dashboards and Alerts
- Selecting granularity intervals (1s, 10s, 1m) for customer activity dashboards based on use case
- Aggregating high-cardinality customer identifiers into anonymized heatmaps for public displays
- Configuring dynamic thresholds for anomaly detection in customer login patterns
- Routing real-time alerts to on-call engineers using escalation policies based on impact severity
- Preserving raw event samples during alert triggers for post-mortem root cause analysis
- Implementing role-based view filters so support leads only see their regional data
- Optimizing dashboard query performance using pre-aggregated materialized views
- Validating dashboard accuracy by reconciling streaming counts with batch truth sources daily
Module 7: Data Governance and Compliance at Scale
- Implementing customer data masking in logs when debugging real-time scoring pipelines
- Enforcing GDPR right-to-erasure across streaming state, caches, and downstream sinks
- Tagging data streams with sensitivity labels for automated policy enforcement
- Auditing access to real-time customer profiles via centralized logging and SIEM integration
- Designing data retention workflows for temporary interaction events (e.g., session clicks)
- Validating consent flags before activating real-time personalization for EU customers
- Conducting DPIAs for new real-time use cases involving biometric or behavioral data
- Coordinating data lineage tracking across streaming jobs for regulatory reporting
Module 8: Performance, Scalability, and Cost Management
- Right-sizing stream processing clusters based on 95th percentile load during peak campaigns
- Implementing autoscaling policies using custom metrics like pending event backlog
- Comparing total cost of ownership between managed and self-hosted streaming platforms
- Optimizing serialization formats (Avro vs. Protobuf) for network and CPU efficiency
- Sharding customer data by region to meet latency and data sovereignty requirements
- Conducting load tests using synthetic customer traffic that mimics seasonal patterns
- Implementing circuit breakers to halt non-critical analytics during system degradation
- Tracking cost-per-event across ingestion, processing, and storage layers by business unit
Module 9: Cross-Functional Integration and Change Management
- Defining API contracts between data engineering and customer service teams for real-time profile access
- Training frontline agents to interpret real-time risk flags without over-relying on automation
- Coordinating deployment windows with marketing to avoid conflicts during campaign launches
- Establishing incident response playbooks for real-time system outages affecting customer experience
- Documenting data ownership handoffs between digital product and analytics teams
- Facilitating joint sprint planning between DevOps and business units for feature prioritization
- Implementing feedback loops from customer service outcomes to improve real-time models
- Managing stakeholder expectations when real-time insights reveal systemic operational gaps