Description

This curriculum spans the design, deployment, and operational governance of a data catalog, comparable in scope to a multi-phase internal capability program that integrates metadata management across data governance, security, and analytics workflows in large organisations.

Module 1: Foundations of Metadata Architecture

Select metadata standards (e.g., Dublin Core, DCAT, ISO 19115) based on industry compliance requirements and interoperability needs.
Define metadata scope: operational, technical, business, and social metadata based on stakeholder use cases.
Choose between centralized vs. federated metadata repository architectures considering organizational data governance maturity.
Map metadata lineage requirements to support regulatory audits and impact analysis workflows.
Integrate metadata classification models to distinguish between PII, financial, and operational data.
Implement metadata ownership models assigning custodianship to domain data stewards.
Evaluate open metadata specifications (e.g., Open Metadata, OMeta) for vendor-agnostic integration.
Design metadata versioning strategy to track schema and definition changes over time.

Module 2: Data Catalog Platform Selection and Integration

Assess catalog platforms (e.g., Alation, Collibra, Apache Atlas) based on native connector availability for existing data systems.
Define ingestion frequency for batch vs. real-time metadata synchronization from source systems.
Configure API-based metadata extraction from cloud data warehouses (e.g., Snowflake, BigQuery) and data lakes (e.g., Delta Lake).
Negotiate data access permissions with platform owners to enable automated metadata harvesting.
Implement metadata proxy patterns when direct access to source systems is restricted.
Map identity providers (e.g., Okta, Azure AD) to catalog roles for consistent access control.
Validate metadata consistency across hybrid environments (on-prem, cloud, SaaS).
Establish fallback mechanisms for metadata ingestion during source system outages.

Module 3: Metadata Ingestion and Automation

Develop custom metadata extractors for legacy systems lacking native APIs or documentation.
Orchestrate ingestion pipelines using workflow tools (e.g., Airflow, Prefect) with error handling and retry logic.
Normalize schema definitions from heterogeneous sources into a unified catalog model.
Apply parsing rules to extract technical metadata from DDL scripts and ETL job configurations.
Implement change data capture (CDC) for tracking schema evolution in transactional databases.
Use statistical sampling to infer metadata attributes when full scans are impractical.
Validate ingested metadata against predefined quality rules (e.g., completeness, format compliance).
Configure incremental metadata loads to minimize processing overhead on production systems.

Module 4: Business Metadata and Context Enrichment

Design controlled vocabularies and business glossaries aligned with enterprise data definitions.
Implement crowdsourced metadata tagging with moderation workflows to prevent inconsistency.
Link KPIs and business metrics to underlying data assets using semantic associations.
Integrate business ownership information from HR systems to auto-populate data stewards.
Enable subject matter experts to annotate datasets with usage notes and caveats.
Map regulatory requirements (e.g., GDPR, CCPA) to specific data elements in the catalog.
Version business definitions and track approval workflows for regulatory compliance.
Establish review cycles for business metadata to prevent obsolescence.

Module 5: Data Lineage and Impact Analysis

Construct end-to-end lineage maps from source systems to reporting dashboards using parser outputs.
Differentiate between syntactic and semantic lineage based on transformation complexity.
Implement lineage gap analysis to identify systems not covered by automated tracking.
Use lineage data to assess impact of schema changes on dependent reports and models.
Optimize lineage storage using graph databases (e.g., Neo4j) for efficient traversal queries.
Balance lineage granularity: row-level vs. table-level tracking based on performance and use case.
Expose lineage data via API for integration with change management systems.
Validate lineage accuracy through reconciliation with ETL job logs and audit trails.

Module 6: Search, Discovery, and Reuse

Tune search relevance algorithms using field weighting (e.g., table name > column description).
Implement faceted search with filters for data domain, owner, sensitivity, and freshness.
Design dataset recommendation engines based on user role and historical access patterns.
Integrate catalog search into IDEs and BI tools via plugins or embedded widgets.
Track search failure logs to identify missing or poorly described datasets.
Apply query expansion techniques using synonym rings from business glossaries.
Measure reuse rates to assess catalog effectiveness and identify underutilized assets.
Implement dataset deprecation workflows with notification to known consumers.

Module 7: Governance, Security, and Compliance

Enforce metadata access controls aligned with data classification policies (e.g., confidential, public).
Mask sensitive metadata attributes (e.g., PII column names) in search results based on user clearance.
Log all metadata access and modification events for audit trail compliance.
Integrate with data loss prevention (DLP) tools to flag unauthorized metadata exports.
Implement retention policies for metadata logs to meet regulatory requirements.
Conduct periodic access reviews to revoke catalog privileges for inactive users.
Embed regulatory tags (e.g., “SOX-critical”) into metadata for automated compliance reporting.
Coordinate metadata declassification procedures with data lifecycle management policies.

Module 8: Performance, Scalability, and Operations

Size catalog infrastructure based on metadata volume, query load, and SLA requirements.
Implement caching strategies for frequently accessed metadata (e.g., popular tables, glossary terms).
Partition metadata storage by domain or sensitivity to improve query performance.
Monitor ingestion pipeline latency and trigger alerts for processing delays.
Optimize full-text search indexes to reduce response time for complex queries.
Conduct load testing on catalog APIs before integrating with high-volume consumers.
Design backup and disaster recovery procedures for metadata repository databases.
Plan for metadata schema evolution without breaking downstream integrations.

Module 9: Adoption, Metrics, and Continuous Improvement

Define KPIs such as metadata coverage, search success rate, and steward engagement.
Instrument user behavior tracking to identify friction points in catalog workflows.
Conduct quarterly data steward workshops to validate metadata accuracy and completeness.
Integrate catalog usage metrics into enterprise data health dashboards.
Establish feedback loops from data consumers to improve metadata quality.
Align catalog roadmap with enterprise data strategy and technology refresh cycles.
Measure time-to-insight reduction for analytics teams using the catalog.
Iterate on UI/UX based on usability testing with non-technical business users.