Description

This curriculum spans the full lifecycle of a metadata repository initiative, equivalent in scope to a multi-phase enterprise implementation involving strategic assessment, platform design, integration engineering, governance structuring, and operational sustainment.

Module 1: Strategic Assessment of Metadata Repository Needs

Evaluate existing data governance maturity using industry frameworks (e.g., DAMA DMBOK) to determine repository scope and integration depth.
Map stakeholder data lineage requirements across business units to identify critical data assets requiring metadata capture.
Assess compatibility of current ETL/ELT pipelines with candidate metadata repository platforms (e.g., Apache Atlas, Informatica Axon).
Define metadata ownership models by department, balancing centralized control with decentralized contribution.
Conduct interviews with data stewards, engineers, and compliance officers to prioritize metadata use cases (e.g., regulatory reporting, impact analysis).
Document technical debt in current metadata practices, including shadow inventories and inconsistent tagging conventions.
Establish criteria for distinguishing operational from analytical metadata based on SLA and refresh frequency.
Develop a phased rollout strategy to avoid disrupting existing data operations during repository deployment.

Module 2: Platform Selection and Architecture Design

Compare open-source versus commercial metadata repository solutions based on API extensibility, support SLAs, and audit logging capabilities.
Design metadata ingestion architecture considering batch, streaming, and on-demand collection patterns.
Specify data model requirements for custom entity types (e.g., AI model versions, feature stores) beyond standard table/column definitions.
Integrate repository schema with existing enterprise data models to ensure semantic consistency.
Implement role-based access control (RBAC) at the metadata attribute level to comply with data classification policies.
Architect high-availability and disaster recovery for the metadata store, including backup frequency and retention.
Select indexing strategy for metadata search performance, balancing latency and storage cost.
Define API contracts for third-party systems (e.g., BI tools, MDM) to consume metadata programmatically.

Module 3: Metadata Ingestion and Integration Patterns

Configure automated scanners for database catalogs, data lakes, and cloud storage to extract structural metadata.
Develop custom connectors for proprietary systems lacking native metadata APIs.
Implement change data capture (CDC) for metadata to track schema evolution over time.
Normalize naming conventions from disparate sources using transformation rules during ingestion.
Handle metadata conflicts from overlapping sources (e.g., data dictionary vs. ETL logs) using conflict resolution policies.
Orchestrate ingestion workflows using tools like Apache Airflow to manage dependencies and error handling.
Validate completeness and accuracy of ingested metadata through reconciliation checks against source systems.
Apply data quality rules to metadata itself (e.g., required descriptions, owner assignments).

Module 4: Business Glossary and Semantic Layer Development

Facilitate workshops to define enterprise-wide business terms with unambiguous definitions and examples.
Link business terms to technical assets (e.g., columns, reports) using explicit mapping rules.
Implement version control for business definitions to track changes and maintain historical context.
Establish approval workflows for new or modified glossary entries involving legal and compliance teams.
Integrate business glossary with data catalog search to enable non-technical users to discover data.
Manage polyhierarchy in glossary structure where terms belong to multiple categories.
Enforce term usage policies through integration with data documentation templates and report footers.
Monitor term adoption rates and update definitions based on user feedback loops.

Module 5: Data Lineage Implementation and Traceability

Distinguish between syntactic and semantic lineage based on available parsing capabilities and business needs.
Implement lineage extraction from SQL scripts, stored procedures, and ETL job configurations.
Resolve incomplete lineage paths due to undocumented transformations or black-box processes.
Visualize end-to-end lineage across hybrid environments (on-prem, cloud, SaaS) with consistent identifiers.
Support impact analysis use cases by enabling backward tracing from reports to source systems.
Optimize lineage storage using graph compression techniques for large-scale environments.
Validate lineage accuracy through sample-based testing against known data flows.
Expose lineage data via API for integration with change management and auditing systems.

Module 6: Governance, Stewardship, and Policy Enforcement

Define metadata steward roles with clear responsibilities for review, approval, and maintenance.
Implement automated policy checks (e.g., PII flagging) using metadata tagging and classification rules.
Enforce metadata completeness as a gate in CI/CD pipelines for data pipeline deployments.
Establish SLAs for metadata update latency relative to source system changes.
Integrate metadata repository with enterprise policy management systems for unified compliance tracking.
Conduct periodic metadata audits to detect drift from governance standards.
Manage metadata retention and archival in alignment with data retention policies.
Document exceptions to metadata policies with justification and expiration dates.

Module 7: Advanced Metadata Use Cases in AI and Analytics

Track feature lineage from raw data to model input, including transformation logic and drift metrics.
Store model metadata (e.g., training dataset, hyperparameters, evaluation scores) in the repository.
Link data quality metrics to specific model performance degradation events for root cause analysis.
Implement metadata tagging for bias indicators and fairness assessments in training data.
Enable model versioning traceability through metadata associations with code repositories and datasets.
Support MLOps workflows by exposing metadata to model monitoring and retraining triggers.
Integrate metadata repository with feature store platforms to maintain consistent feature definitions.
Expose metadata on data drift and concept drift to data science teams via dashboard integrations.

Module 8: Performance Optimization and Operational Maintenance

Monitor ingestion job performance and tune batch sizes to minimize source system load.
Implement metadata caching strategies for high-frequency query endpoints.
Optimize full-text search relevance by tuning analyzers and boosting critical metadata fields.
Scale metadata storage independently based on growth projections for technical and business metadata.
Develop alerting for ingestion failures, latency spikes, and storage threshold breaches.
Plan for schema evolution in the repository itself, including backward compatibility during upgrades.
Conduct定期 load testing on search and lineage query endpoints under realistic workloads.
Document operational runbooks for common failure scenarios and recovery procedures.

Module 9: Change Management and Continuous Improvement

Measure metadata repository adoption using metrics such as active users, search volume, and contribution rates.
Establish feedback channels from data consumers to prioritize new features and fixes.
Conduct quarterly business reviews with data governance council to assess ROI and alignment.
Iterate on metadata models based on evolving analytics and regulatory requirements.
Update training materials and onboarding workflows in response to observed user errors.
Integrate user behavior analytics to identify underutilized or confusing repository features.
Manage deprecation of legacy metadata systems with data migration and redirect strategies.
Align metadata roadmap with enterprise data strategy and technology refresh cycles.