Description

This curriculum spans the technical and operational complexity of a multi-workshop program for building and maintaining real-time integration systems, comparable to the iterative design and governance cycles seen in enterprise data platform modernization initiatives.

Module 1: Architecting Real-Time Data Pipelines for Process Integration

Selecting between message brokers (e.g., Kafka, RabbitMQ) based on throughput requirements, message durability, and replay capability in high-volume transaction environments.
Designing schema evolution strategies for event payloads to maintain backward compatibility during integration point upgrades.
Implementing idempotent consumers to prevent data duplication when processing retried or duplicated integration events.
Choosing between push and pull ingestion models based on source system capabilities and latency SLAs.
Partitioning event streams to enable parallel processing while preserving per-entity ordering guarantees.
Configuring dead-letter queues and monitoring for failed message routing in asynchronous integration pipelines.

Module 2: Event-Driven Integration Patterns in Heterogeneous Systems

Mapping canonical event schemas to domain-specific formats when integrating legacy ERP systems with modern cloud platforms.
Implementing event enrichment using reference data lookups without introducing blocking dependencies on external services.
Deciding when to use process managers (sagas) versus choreography for coordinating multi-step business transactions.
Handling partial failures in distributed workflows by designing compensating actions for rollback semantics.
Integrating batch-originated data into real-time streams using micro-batch ingestion with watermarking for time consistency.
Managing event versioning across integration touchpoints during phased system migrations.

Module 3: Real-Time Data Transformation and Stream Processing

Choosing stateful versus stateless transformations based on the need for session windows or cumulative aggregations.
Implementing time-windowed aggregations (tumbling, sliding, session) with considerations for late-arriving data.
Optimizing stream processing topology to minimize serialization overhead and reduce inter-node communication.
Validating and filtering malformed events at ingestion to prevent pipeline contamination.
Using CEP (Complex Event Processing) rules to detect business-relevant patterns such as order bursts or failed logins.
Scaling stream processing jobs by repartitioning data based on business key distribution to avoid hotspots.

Module 4: Low-Latency Data Storage and Access Patterns

Selecting between in-memory data grids and time-series databases based on query patterns and data retention requirements.
Designing secondary indexes on streaming data stores to support real-time lookup by non-primary keys.
Implementing change data capture (CDC) from OLTP databases with minimal impact on transaction performance.
Configuring TTL and compaction strategies in real-time data stores to balance query performance with storage cost.
Denormalizing data at write time to eliminate join operations during real-time reporting queries.
Partitioning and sharding strategies for distributed databases to support high-concurrency read workloads.

Module 5: Real-Time Reporting and Dashboarding Infrastructure

Choosing between direct querying of stream stores versus maintaining materialized views for reporting consistency.
Implementing incremental refresh mechanisms in dashboards to reduce load on backend systems.
Securing real-time dashboards with row-level access controls based on organizational hierarchy.
Designing dashboard SLAs for data freshness and aligning them with underlying pipeline capabilities.
Handling high-cardinality dimensions in real-time reports without degrading query performance.
Integrating real-time alerts into reporting tools using threshold-based triggers on streaming metrics.

Module 6: Observability and Operational Monitoring of Integration Flows

Instrumenting message processing pipelines with distributed tracing to diagnose latency bottlenecks.
Defining and collecting SLOs for end-to-end event delivery latency across integration hops.
Correlating log entries across microservices using shared trace identifiers in event headers.
Setting up anomaly detection on message throughput to identify integration failures before alerting.
Monitoring consumer lag in message queues to proactively scale processing capacity.
Archiving diagnostic data from integration pipelines for audit and post-incident analysis.

Module 7: Governance, Security, and Compliance in Real-Time Integrations

Implementing end-to-end encryption for sensitive business events in transit and at rest.
Applying data masking or tokenization in real-time pipelines to comply with privacy regulations.
Enforcing schema validation at integration boundaries to prevent invalid data propagation.
Managing access to integration endpoints using OAuth2 scopes and service identity tokens.
Documenting data lineage across real-time flows to support regulatory audits and impact analysis.
Establishing change control procedures for modifying production integration pipelines with rollback plans.

Module 8: Performance Optimization and Scalability Engineering

Tuning batch sizes and buffering intervals in producers to balance latency and throughput.
Implementing backpressure handling in consumers to prevent system overload during traffic spikes.
Right-sizing cluster resources for stream processing engines based on historical load patterns.
Using data sampling techniques for debugging high-volume pipelines without full replication.
Precomputing aggregations at multiple granularities to support both real-time and near-real-time reporting.
Conducting load testing on integration pipelines using production-like data distributions and concurrency levels.