Description

This curriculum spans the technical depth and operational rigor of a multi-workshop engineering program focused on production-grade stream processing, comparable to an internal capability build for real-time data platforms in large-scale organisations.

Module 1: Architecting Real-Time Data Ingestion Pipelines

Selecting between push-based (Kafka, Kinesis) and pull-based (Fluentd, Logstash) ingestion based on source system capabilities and latency SLAs
Designing partitioning strategies in message queues to balance throughput, ordering guarantees, and parallel processing
Implementing schema validation at ingestion to prevent downstream processing failures from malformed or schema-evolved data
Configuring backpressure mechanisms to prevent consumer overload during traffic spikes
Integrating authentication and encryption (TLS, SASL) for secure data transfer across network boundaries
Handling ingestion from heterogeneous sources including IoT devices, mobile apps, and legacy databases with varying update frequencies
Choosing between batch micro-batching and true event-by-event processing based on throughput and latency requirements

Module 2: Stream Processing Frameworks and Execution Models

Comparing stateful processing capabilities in Flink, Spark Streaming, and Kafka Streams for windowed aggregations and session tracking
Configuring checkpointing intervals in Flink to balance fault tolerance overhead and recovery time objectives (RTO)
Managing operator chaining and task slot allocation to optimize resource utilization and reduce serialization overhead
Implementing custom watermarks to handle late-arriving data in out-of-order streams
Choosing between at-least-once and exactly-once processing semantics based on business impact of duplication vs. loss
Designing side outputs for handling malformed records and exceptions without disrupting main processing flow
Optimizing garbage collection behavior in long-running JVM-based stream processors through heap sizing and GC tuning

Module 3: State Management and Fault Tolerance

Designing state backend selection (RocksDB vs. heap) based on state size, access patterns, and recovery speed requirements
Implementing state TTL (time-to-live) policies to prevent unbounded state growth in session windows
Partitioning keyed state to align with data distribution and avoid hotspots
Planning for state migration during schema changes using savepoints and versioned serializers
Validating state consistency after failover by comparing pre- and post-recovery checkpoints
Monitoring state size growth and triggering alerts when thresholds approach storage limits
Securing state snapshots stored in cloud storage with encryption and access controls

Module 4: Real-Time Data Enrichment and Transformation

Joining streaming events with slowly changing dimension data from external databases using async I/O patterns
Caching reference data in broadcast state to minimize external lookup latency and database load
Handling schema evolution in reference datasets and implementing backward-compatible transformation logic
Applying data masking or tokenization during transformation for compliance with privacy regulations
Implementing retry logic with exponential backoff for transient failures in external service calls
Validating enriched output against business rules before forwarding to downstream systems
Measuring end-to-end transformation latency to identify bottlenecks in enrichment pipelines

Module 5: Windowing and Time Semantics

Selecting between event time, ingestion time, and processing time based on data source reliability and use case accuracy needs
Configuring sliding, tumbling, and session windows to match business reporting cycles and user behavior patterns
Setting allowed lateness duration for windowed operations to balance completeness and timeliness
Triggering early results in windows for time-sensitive alerts while maintaining final accuracy
Handling time zone differences in global event timestamps during window assignment
Debugging window misalignments caused by clock skew across distributed systems
Validating window output consistency across reprocessing scenarios using deterministic logic

Module 6: Monitoring, Alerting, and Observability

Instrumenting custom metrics for business KPIs alongside system metrics like lag and throughput
Setting dynamic alert thresholds based on historical patterns to reduce false positives
Correlating logs, metrics, and traces to diagnose performance degradation in multi-stage pipelines
Implementing health checks for external dependencies to detect cascading failures early
Designing dashboards that distinguish between infrastructure issues and data quality anomalies
Using synthetic transactions to validate pipeline correctness in production environments
Archiving diagnostic data for post-mortem analysis while managing storage costs

Module 7: Scalability and Resource Management

Right-sizing cluster resources based on peak load patterns and cost-performance trade-offs
Configuring auto-scaling policies using custom metrics beyond CPU (e.g., consumer lag)
Managing parallelism settings per operator to align with data skew and processing complexity
Reserving resources for critical pipelines in shared clusters to ensure SLA compliance
Implementing circuit breakers to isolate failing components and prevent resource exhaustion
Conducting load testing with production-like data volumes to validate scaling assumptions
Balancing state backend performance with disk I/O costs in cloud environments

Module 8: Data Quality and Governance in Streaming Systems

Implementing schema registry enforcement to prevent uncontrolled schema drift in Kafka topics
Tracking data lineage across real-time transformations for audit and debugging purposes
Automating anomaly detection in data distributions (e.g., null rates, value ranges) using statistical baselines
Applying data retention policies to streaming topics based on regulatory and business requirements
Documenting data ownership and stewardship for real-time streams in the data catalog
Enforcing PII detection and redaction at ingestion for compliance with GDPR and CCPA
Coordinating schema changes across producers and consumers using versioning and backward compatibility checks

Module 9: Integration with Batch and Serving Layers

Implementing the lambda architecture with unified APIs to serve real-time and batch views consistently
Synchronizing state between stream processors and serving databases using changelog streams
Backfilling historical data into real-time pipelines during system migrations or reprocessing
Designing dual writes to stream and database with conflict resolution strategies for consistency
Using materialized views in data warehouses updated via streaming changefeeds for low-latency queries
Validating consistency between real-time aggregates and batch recalculations during reconciliation windows
Managing schema synchronization between streaming topics and data lake storage formats (Parquet, ORC)