Description

This curriculum spans the technical and organisational challenges of integrating data systems across a large enterprise, comparable to a multi-workshop program addressing real-world complexities like event-driven architectures, cross-system data governance, and the operationalisation of secure, observable pipelines in heterogeneous environments.

Module 1: Assessing Integration Readiness Across Heterogeneous Data Environments

Evaluate legacy system APIs for compatibility with modern data exchange protocols such as REST or gRPC.
Inventory data silos across departments and classify them by update frequency, ownership, and access controls.
Map data lineage from source systems to downstream consumers to identify undocumented dependencies.
Assess data freshness requirements for operational versus analytical use cases.
Determine ownership boundaries for master data entities such as customer, product, or location.
Conduct technical feasibility studies on retrofitting change data capture (CDC) into non-instrumented databases.
Identify systems with embedded business logic that may conflict with centralized integration rules.
Document constraints imposed by third-party vendor systems on data extraction frequency and format.

Module 2: Designing Event-Driven Integration Architectures

Select messaging middleware (e.g., Kafka, RabbitMQ, AWS EventBridge) based on throughput, durability, and replay requirements.
Define event schemas using Avro or Protobuf and enforce schema evolution policies in a registry.
Implement idempotent consumers to handle duplicate event delivery in distributed systems.
Design dead-letter queues and monitoring for failed event processing with root cause classification.
Determine event partitioning strategies to balance load while preserving message order where required.
Integrate event sourcing with existing CRUD-based systems using dual-write patterns and compensating transactions.
Configure message retention policies based on compliance, debugging, and recovery needs.
Implement circuit breakers and backpressure mechanisms to prevent cascading failures in event pipelines.

Module 3: Master Data Management and Identity Resolution

Choose between centralized MDM hubs and registry-style federated models based on organizational autonomy.
Design golden record creation logic using survivorship rules for conflicting attribute values.
Implement probabilistic matching algorithms with tunable thresholds for entity resolution.
Integrate MDM with identity providers to synchronize user roles and access rights.
Define stewardship workflows for manual review of high-confidence match candidates.
Map source system identifiers to global IDs using cross-reference tables with audit trails.
Enforce data quality rules at the point of entry into the MDM system.
Design versioning and rollback capabilities for golden record changes.

Module 4: Building Secure and Compliant Data Pipelines

Implement field-level encryption for sensitive data in transit and at rest using KMS-managed keys.
Apply dynamic data masking based on user roles and session context in query results.
Embed audit logging into pipeline components to track data access and transformation steps.
Integrate with enterprise IAM systems for centralized authentication and authorization.
Classify data elements according to sensitivity and map controls to regulatory frameworks (e.g., GDPR, HIPAA).
Design data retention and deletion workflows that propagate across integrated systems.
Conduct data protection impact assessments (DPIAs) for new integration flows involving personal data.
Implement tokenization for payment and identity data to reduce scope of compliance audits.

Module 5: Orchestration and Workflow Management at Scale

Select orchestration engines (e.g., Airflow, Prefect, Argo) based on scheduling complexity and UI requirements.
Design DAGs with explicit failure handling, retries, and alerting on SLA misses.
Parameterize workflows to support multi-tenant or environment-specific execution.
Implement state management for long-running processes using durable execution frameworks.
Integrate orchestration logs with centralized monitoring and tracing systems.
Version control workflow definitions and coordinate deployment via CI/CD pipelines.
Manage resource contention by scheduling high-load jobs during off-peak windows.
Implement health checks and dependency validation before workflow initiation.

Module 6: Real-Time Data Synchronization and Change Propagation

Configure database transaction log readers for low-latency CDC without impacting source performance.
Handle schema evolution in source databases and propagate changes to downstream consumers.
Design conflict resolution strategies for bi-directional sync in multi-master setups.
Implement backfill mechanisms for new subscribers to catch up on historical changes.
Monitor replication lag and trigger alerts when thresholds exceed business SLAs.
Use watermarking to ensure consistency across distributed event consumers.
Optimize payload size by filtering irrelevant tables or columns at the extraction layer.
Validate data consistency between source and target using automated reconciliation jobs.

Module 7: Observability and Performance Monitoring in Integrated Systems

Instrument integration components with structured logging and distributed tracing (e.g., OpenTelemetry).
Define SLOs for data latency, availability, and accuracy across integration touchpoints.
Build dashboards that correlate pipeline performance with business process KPIs.
Implement synthetic transactions to proactively detect integration failures.
Set up anomaly detection on data volume and rate metrics to identify upstream disruptions.
Trace end-to-end data flow across systems to isolate bottlenecks in transformation logic.
Archive and index diagnostic data for post-incident analysis and regulatory inquiries.
Standardize metric naming and tagging conventions across integration teams.

Module 8: Governance, Metadata Management, and Cataloging

Deploy a centralized metadata repository to catalog datasets, schemas, and pipeline dependencies.
Automate metadata extraction from ETL jobs, databases, and API definitions.
Link technical metadata to business glossaries using semantic tagging.
Implement data ownership and stewardship attribution in the catalog.
Enforce metadata completeness as a gate in CI/CD pipelines for new data assets.
Integrate data quality metrics into the catalog for consumer transparency.
Design retention policies for metadata based on audit and discovery requirements.
Enable API-driven access to metadata for integration testing and impact analysis.

Module 9: Managing Technical Debt and Evolution in Integration Landscapes

Conduct integration architecture reviews to identify point-to-point coupling and duplication.
Refactor brittle batch jobs into reusable, parameterized services with versioned APIs.
Plan incremental migration from legacy ETL to modern data mesh or fabric patterns.
Document integration anti-patterns observed in production and establish design review gates.
Balance reuse versus customization when integrating off-the-shelf applications.
Retire deprecated interfaces with backward-compatible adapters and deprecation timelines.
Standardize data contracts between teams to reduce integration onboarding time.
Measure integration health using metrics such as incident frequency, mean time to repair, and test coverage.