Description

This curriculum spans the design and operationalization of metadata repositories across enterprise data ecosystems, comparable in scope to a multi-workshop program for building internal data governance capabilities, covering the full lifecycle from ingestion and quality control to compliance and stewardship workflows.

Module 1: Foundations of Metadata Architecture in Enterprise Systems

Define metadata domain boundaries across operational, analytical, and AI/ML systems to prevent scope creep in repository design.
Select metadata storage models (graph, relational, document) based on query patterns and lineage traversal requirements.
Establish ownership models for technical, business, and operational metadata to clarify stewardship responsibilities.
Integrate metadata schema standards (e.g., DCAT, ISO 11179) with internal data dictionary conventions.
Design metadata versioning strategies to support backward compatibility during schema evolution.
Implement metadata lifecycle states (draft, approved, deprecated) with audit trails for compliance.
Evaluate metadata harvesting frequency based on source system volatility and business SLAs.
Map metadata attributes to regulatory requirements (e.g., GDPR, CCPA) during initial schema design.

Module 2: Metadata Ingestion and Integration Patterns

Configure batch vs. streaming ingestion pipelines based on source system capabilities and metadata freshness needs.
Normalize metadata from heterogeneous sources (databases, ETL tools, BI platforms) using canonical models.
Handle authentication and credential management for metadata extractors accessing secured systems.
Design fault-tolerant ingestion workflows with retry logic and dead-letter queue handling.
Implement metadata change detection using checksums, timestamps, or CDC mechanisms.
Resolve naming conflicts during metadata integration using namespace isolation or prefixing rules.
Validate metadata payloads against schema contracts before ingestion to prevent repository corruption.
Orchestrate metadata pipelines using workflow engines (e.g., Airflow, Prefect) with monitoring hooks.

Module 3: Metadata Quality and Validation Frameworks

Define metadata completeness thresholds (e.g., required fields per asset type) for production readiness.
Implement automated validation rules for metadata consistency (e.g., foreign key references in lineage).
Track metadata accuracy by comparing repository entries against source system catalogs.
Establish feedback loops for data stewards to correct metadata discrepancies via UI or API.
Quantify metadata timeliness using ingestion-to-availability latency metrics.
Design reconciliation jobs to detect and report metadata drift across systems.
Enforce data type and format constraints on metadata attributes during ingestion.
Assign metadata quality scores to assets for risk-based prioritization of remediation.

Module 4: Metadata Governance and Stewardship Workflows

Configure role-based access controls for metadata creation, modification, and approval actions.
Implement workflow engines for metadata change requests requiring multi-party approvals.
Define escalation paths for unresolved metadata disputes between business and technical teams.
Automate stewardship notifications for metadata assets approaching deprecation.
Enforce metadata publishing policies based on data classification and sensitivity levels.
Log all stewardship actions for audit purposes, including rationale for metadata decisions.
Integrate stewardship tasks with enterprise issue tracking systems (e.g., Jira, ServiceNow).
Measure stewardship workload distribution to identify bottlenecks in governance processes.

Module 5: Lineage and Dependency Management

Extract lineage from ETL/ELT execution logs using parser rules tailored to specific tools (e.g., Informatica, dbt).
Resolve indirect dependencies through SQL parsing when direct lineage is unavailable.
Store lineage at multiple granularities (table, column, field-level) based on compliance needs.
Implement impact analysis queries to identify downstream consumers before schema changes.
Handle lineage gaps due to uninstrumented processes using manual annotation workflows.
Version lineage graphs to support historical impact analysis for regulatory audits.
Optimize lineage traversal performance using graph database indexing strategies.
Validate lineage accuracy by comparing inferred relationships with documented workflows.

Module 6: Semantic Layer and Business Metadata Management

Model business glossaries with term hierarchies, synonyms, and cross-domain mappings.
Link business terms to technical assets using bidirectional traceability.
Enforce term deprecation policies with notification timelines for dependent teams.
Implement search ranking logic that prioritizes approved, high-quality business definitions.
Manage multilingual business metadata with translation workflows and language tags.
Integrate business metadata with BI semantic layers (e.g., LookML, Power BI metrics).
Track term usage across reports and dashboards to assess business impact.
Resolve conflicting business definitions through governance committee workflows.

Module 7: Metadata Search, Discovery, and Access

Index metadata attributes using full-text search engines (e.g., Elasticsearch) with custom analyzers.
Implement faceted search with filters for data domain, owner, sensitivity, and freshness.
Rank search results using relevance signals such as usage frequency and metadata completeness.
Design autocomplete and type-ahead features based on user query logs and popularity.
Integrate metadata search with IDEs and data science notebooks via API endpoints.
Implement query expansion using synonym graphs and business glossary mappings.
Log user search behavior to refine ranking algorithms and identify discovery gaps.
Enforce attribute-level masking in search results based on user access rights.

Module 8: Metadata Operations and System Reliability

Monitor metadata ingestion pipeline latency and error rates using observability tools.
Design backup and recovery procedures for metadata repositories with point-in-time restore.
Implement automated consistency checks between metadata and source system inventories.
Scale metadata APIs using caching, pagination, and rate limiting for enterprise loads.
Conduct disaster recovery drills to validate metadata restoration SLAs.
Optimize database performance for large-scale lineage queries using materialized views.
Manage metadata schema migrations with zero-downtime deployment strategies.
Instrument metadata services with structured logging for root cause analysis.

Module 9: Regulatory Compliance and Audit Readiness

Map metadata attributes to specific regulatory controls (e.g., SOX, HIPAA, MiFID II).
Generate audit reports showing metadata change history for regulated data elements.
Implement data retention policies for metadata based on legal hold requirements.
Produce lineage documentation for data used in financial reporting or risk models.
Configure immutable audit logs for metadata access and modification events.
Support data subject access requests (DSARs) using metadata-driven data location maps.
Validate metadata completeness for personally identifiable information (PII) tagging.
Coordinate metadata audits with internal compliance teams using standardized checklists.