This curriculum spans the technical and organisational challenges of building and maintaining enterprise-scale metadata repositories, comparable in scope to a multi-phase internal capability program for data governance and metadata engineering.
Module 1: Architecting Metadata Repository Infrastructure
- Selecting between centralized, federated, and hybrid metadata repository topologies based on organizational data distribution and governance requirements.
- Evaluating storage backends (relational, graph, NoSQL) for metadata persistence based on query patterns and lineage traversal performance.
- Designing schema evolution strategies to accommodate changing metadata standards without breaking downstream consumers.
- Implementing high availability and disaster recovery configurations for metadata services in regulated environments.
- Integrating identity providers (LDAP, SAML) for role-based access control at the repository level.
- Configuring metadata indexing strategies to balance query responsiveness with ingestion latency.
- Establishing capacity planning benchmarks for metadata growth based on source system inventory and refresh frequency.
- Deploying metadata services in containerized environments with orchestration (Kubernetes) for scalability.
Module 2: Ingestion Frameworks for Heterogeneous Data Sources
- Developing custom connectors for legacy systems lacking native metadata export APIs.
- Implementing incremental metadata extraction to minimize load on production databases.
- Mapping technical metadata (column names, data types) from diverse RDBMS platforms into a unified model.
- Handling authentication and credential management for secure access to cloud data warehouses.
- Normalizing metadata from semi-structured sources (JSON, XML) into relational metadata schemas.
- Validating completeness and consistency of ingested metadata using checksums and row count reconciliation.
- Orchestrating ingestion pipelines with dependency management across interdependent source systems.
- Applying data masking rules during ingestion for sensitive metadata elements.
Module 3: Semantic Layer Design and Business Glossary Integration
- Aligning business terms in the glossary with technical metadata through explicit term-to-column mappings.
- Resolving term ownership conflicts across business units during glossary consolidation.
- Implementing versioning for business definitions to support auditability and change tracking.
- Enforcing term classification hierarchies (e.g., PII, Financial) with validation rules.
- Integrating glossary change workflows with enterprise change management systems.
- Linking business metrics definitions to underlying transformation logic in ETL jobs.
- Designing search relevance rules to prioritize contextually appropriate terms in user queries.
- Automating synonym management and cross-referencing between regional business units.
Module 4: Automated Lineage Extraction and Impact Analysis
- Extracting transformation logic from SQL scripts using parser-based tools to build column-level lineage.
- Reconciling lineage gaps in compiled or obfuscated ETL workflows (e.g., SSIS, Informatica).
- Modeling indirect data flows caused by staging tables or temporary datasets.
- Implementing lineage confidence scoring based on source reliability and parsing completeness.
- Generating upstream/downstream impact reports for schema deprecation initiatives.
- Visualizing lineage graphs with filtering controls to manage complexity in large environments.
- Storing lineage as directed acyclic graphs (DAGs) to support path traversal queries.
- Handling lineage for real-time streaming pipelines with ephemeral data states.
Module 5: Data Quality Metadata and Observability Integration
- Instrumenting data pipelines to emit DQ rule outcomes as metadata events.
- Correlating data quality rule violations with specific source columns and ingestion batches.
- Storing historical DQ metrics for trend analysis and SLA reporting.
- Linking data quality rules to business glossary terms for contextual remediation.
- Triggering metadata status flags (e.g., “untrusted”) based on DQ threshold breaches.
- Integrating profiling statistics (null rates, value distributions) into column metadata.
- Mapping data quality rules to regulatory requirements (e.g., BCBS 239, GDPR).
- Designing retention policies for transient DQ metadata to manage storage costs.
Module 6: Metadata Transformation and Standardization Pipelines
- Developing transformation rules to convert vendor-specific metadata formats into canonical models.
- Implementing data type harmonization across heterogeneous source systems (e.g., VARCHAR vs STRING).
- Applying naming convention standardization to technical artifacts during ingestion.
- Resolving schema conflicts when merging metadata from overlapping data domains.
- Building reusable transformation templates for common metadata enrichment patterns.
- Validating transformed metadata against schema conformance rules before repository load.
- Logging transformation errors with context for operator review and correction.
- Orchestrating transformation pipelines with idempotent execution for reprocessing.
Module 7: Access Control, Auditability, and Regulatory Compliance
- Implementing attribute-based access control (ABAC) for metadata elements containing sensitive classifications.
- Masking metadata fields (e.g., column descriptions with PII references) based on user roles.
- Logging all metadata read and modification operations for audit trail compliance.
- Enabling time-travel queries on metadata to support regulatory point-in-time reporting.
- Integrating metadata change logs with SIEM systems for security monitoring.
- Applying data retention and deletion workflows to metadata in response to DSARs.
- Documenting metadata handling procedures for external auditor review.
- Classifying metadata assets by sensitivity level to enforce encryption and transmission policies.
Module 8: Performance Optimization and Scalability Engineering
- Tuning database indexes on metadata repositories for high-frequency query patterns (e.g., lineage lookup).
- Implementing caching layers for frequently accessed metadata entities (e.g., business terms).
- Partitioning metadata tables by domain or ingestion date to improve query performance.
- Optimizing API response payloads by supporting field-level metadata retrieval.
- Load testing metadata services under concurrent user scenarios to identify bottlenecks.
- Sharding metadata storage across clusters based on data domain ownership.
- Compressing historical metadata snapshots to reduce storage footprint.
- Monitoring garbage collection and memory usage in metadata application servers.
Module 9: Interoperability and Metadata Exchange Standards
- Implementing Open Metadata APIs (e.g., Apache Atlas, OpenMetadata) for cross-platform queries.
- Translating metadata between proprietary formats and open standards (e.g., JSON Schema, DCAT).
- Configuring metadata federation to enable cross-repository search without data duplication.
- Validating metadata exports against schema standards (e.g., ISO 11179, Dublin Core).
- Establishing metadata sharing SLAs with partner organizations for joint data initiatives.
- Handling version mismatches in metadata exchange protocols during system upgrades.
- Signing and verifying metadata payloads to ensure integrity in external exchanges.
- Mapping security classifications during metadata exchange to enforce recipient restrictions.