Description

This curriculum spans the design and operationalization of metadata repositories with the same technical specificity and governance rigor found in multi-workshop enterprise data governance programs, covering architecture, ingestion, security, and lifecycle management across complex data ecosystems.

Module 1: Defining Metadata Governance Frameworks

Selecting metadata classification schemes (technical, operational, business) based on enterprise data lineage requirements
Establishing ownership models for metadata assets across data stewards, IT, and business units
Mapping regulatory compliance obligations (e.g., GDPR, SOX) to metadata retention and access policies
Choosing between centralized versus federated governance based on organizational maturity and data sprawl
Integrating metadata governance into existing data governance councils with defined escalation paths
Defining SLAs for metadata accuracy, timeliness, and completeness across critical data domains
Documenting metadata change approval workflows with audit trail requirements
Aligning metadata policies with enterprise data catalog taxonomy standards

Module 2: Metadata Repository Architecture Design

Selecting repository storage engines (relational, graph, NoSQL) based on query patterns and lineage depth
Designing schema models for storing technical metadata from heterogeneous sources (databases, ETL, APIs)
Implementing soft delete mechanisms to preserve historical metadata states without data loss
Configuring indexing strategies for metadata attributes frequently used in impact analysis
Deciding on in-memory caching layers for high-frequency metadata queries
Architecting multi-tenancy support for shared repository usage across business units
Designing partitioning strategies for metadata tables based on ingestion frequency and retention
Specifying API rate limits and concurrency controls for metadata access services

Module 3: Metadata Ingestion and Integration

Choosing between push and pull ingestion models based on source system capabilities
Implementing incremental metadata extraction to minimize source system load
Developing parsers for proprietary ETL tool metadata exports with version compatibility
Handling schema drift detection during ingestion from streaming and log-based sources
Validating metadata payload completeness before ingestion using schema contracts
Configuring retry logic and dead-letter queues for failed ingestion jobs
Mapping disparate naming conventions from source systems to a unified canonical model
Embedding data quality rules within ingestion pipelines to flag invalid metadata entries

Module 4: Metadata Lineage and Provenance Tracking

Defining granularity levels for lineage (column-level vs. table-level) based on regulatory needs
Implementing automated parsing of SQL scripts to extract transformation logic for lineage maps
Resolving ambiguous lineage when multiple sources contribute to a single target field
Storing and querying temporal lineage to support point-in-time impact analysis
Integrating lineage data from third-party ETL tools via proprietary SDKs or log parsing
Handling lineage gaps due to undocumented manual data interventions
Optimizing graph traversal performance for deep lineage queries across thousands of nodes
Enforcing lineage capture requirements during CI/CD deployment of data pipelines

Module 5: Metadata Quality Management

Defining metadata completeness thresholds for critical data elements (e.g., description, owner, PII flag)
Creating automated validation rules to detect stale metadata (e.g., unchanged in 12+ months)
Implementing scoring models to quantify metadata quality across domains
Scheduling recurring metadata quality audits with exception reporting workflows
Configuring alerts for missing technical metadata after pipeline deployment
Enforcing mandatory metadata fields during data asset registration processes
Tracking remediation progress for metadata quality issues with ownership assignment
Integrating metadata quality metrics into executive data health dashboards

Module 6: Access Control and Metadata Security

Implementing attribute-based access control (ABAC) for sensitive metadata fields
Masking PII-related metadata attributes based on user role and clearance level
Integrating with enterprise identity providers (e.g., Active Directory, SSO) for authentication
Auditing metadata access patterns to detect unauthorized exploration behavior
Enforcing encryption of metadata at rest and in transit using organizational standards
Managing API key lifecycle for programmatic metadata access by data pipelines
Applying row-level security to restrict visibility of business-unit-specific metadata
Documenting data classification mappings used to auto-apply metadata access policies

Module 7: Metadata Lifecycle and Retention

Defining metadata retention periods aligned with source data retention policies
Implementing automated archiving of deprecated metadata assets to cold storage
Tracking metadata deprecation timelines in coordination with data pipeline sunsetting
Preserving lineage context for retired systems required for audit purposes
Executing metadata purging workflows with legal hold overrides
Versioning metadata schemas to support backward compatibility during upgrades
Managing dependencies between metadata objects to prevent premature deletion
Logging all metadata lifecycle transitions for compliance audit trails

Module 8: Monitoring, Alerting, and Operations

Instrumenting ingestion pipelines with health checks and latency monitoring
Setting up alerts for metadata repository performance degradation (e.g., query timeouts)
Tracking metadata drift between source systems and the repository
Creating dashboards for metadata coverage by data domain and system
Establishing incident response procedures for metadata corruption events
Automating backup and recovery testing for metadata schema and data
Measuring and reporting on metadata synchronization lag across systems
Conducting root cause analysis for recurring metadata quality incidents

Module 9: Integration with Data Management Ecosystems

Exposing metadata via standardized APIs (e.g., Open Metadata, REST) for downstream tools
Synchronizing metadata with data catalogs, BI platforms, and data quality tools
Embedding metadata context into data pipeline observability and monitoring tools
Feeding metadata into automated data documentation generators
Integrating with data lineage tools to enrich end-to-end traceability
Supporting data discovery tools with semantic metadata and tagging
Providing metadata snapshots for offline audit and regulatory submission
Enabling CI/CD pipelines to validate metadata compliance before deployment