Description

This curriculum spans the design and operationalisation of metadata ingestion systems with the breadth and technical specificity of a multi-phase data governance rollout, covering architecture decisions, pipeline engineering, and stewardship workflows typical of large-scale catalog implementations.

Module 1: Defining Metadata Scope and Classification Frameworks

Select metadata domains (technical, operational, business, social) based on stakeholder query patterns and lineage requirements.
Establish metadata classification taxonomies aligned with existing data governance policies and enterprise data models.
Decide whether to adopt open standards (e.g., DCAT, Dublin Core) or proprietary classification schemas based on interoperability needs.
Implement metadata tagging conventions for data sources, including versioning, ownership, and sensitivity labels.
Balance granularity of metadata capture against storage and processing overhead in high-volume environments.
Define metadata ownership roles per domain and integrate with IAM policies for attribute-level access control.
Design backward-compatible classification updates to prevent pipeline breakage during schema evolution.
Map metadata attributes to regulatory requirements (e.g., GDPR, CCPA) for automated compliance reporting.

Module 2: Evaluating and Selecting Metadata Repository Platforms

Compare graph-based (e.g., Neo4j) vs. document-based (e.g., MongoDB) vs. relational storage for metadata relationship density.
Assess native support for metadata standards (e.g., Apache Atlas, OpenMetadata, Alation) versus custom-built solutions.
Validate platform scalability under concurrent ingestion from 50+ source systems with metadata bursts.
Test API rate limits and authentication mechanisms for third-party tool integrations (e.g., ETL, BI, MDM).
Evaluate vendor lock-in risks when using cloud-managed metadata services with proprietary APIs.
Measure time-to-query performance for lineage traversal across 10+ hop dependencies.
Determine support for temporal metadata (schema and value changes over time) in candidate platforms.
Verify audit logging capabilities for metadata modification events at field level.

Module 3: Designing Metadata Ingestion Pipelines

Choose between batch ingestion (scheduled) and event-driven (Kafka-based) models based on freshness SLAs.
Implement idempotent ingestion logic to handle duplicate metadata payloads from source retries.
Develop transformation rules to normalize inconsistent naming conventions from heterogeneous sources.
Integrate retry and dead-letter queue mechanisms for failed metadata records during transmission.
Optimize payload size by compressing large metadata blobs (e.g., query plans, JSON schemas).
Orchestrate ingestion workflows using Airflow or Prefect with dependency-aware scheduling.
Embed lineage context (source system, extractor version, timestamp) into every metadata record.
Apply schema validation against a central metadata contract before ingestion.

Module 4: Extracting Metadata from Heterogeneous Sources

Configure JDBC drivers to extract table DDL, constraints, and index metadata from legacy RDBMS.
Parse DDL scripts from version-controlled repositories when direct database access is restricted.
Use native APIs (e.g., Snowflake Information Schema, BigQuery REST) to pull cloud data warehouse metadata.
Intercept ETL job configurations (e.g., Informatica, Talend) to extract transformation logic and dependencies.
Scrape BI tool metadata (e.g., Tableau workbooks, Power BI models) for semantic layer definitions.
Instrument Spark applications to emit runtime metadata (schema inference, partitioning, skew).
Extract API specifications (OpenAPI) to register data contracts and endpoint-level metadata.
Handle authentication and credential rotation for source systems with short-lived tokens.

Module 5: Implementing Metadata Quality Controls

Define completeness thresholds (e.g., 95% column description coverage) for critical datasets.
Deploy automated checks for stale metadata (e.g., unrefreshed in >30 days) with alerting.
Validate referential integrity between metadata entities (e.g., foreign key to column mapping).
Measure accuracy of inferred lineage by comparing against manually documented workflows.
Implement anomaly detection on metadata change rates to flag potential configuration drift.
Enforce data type consistency across source, staging, and target representations.
Track metadata defect resolution SLAs across stewardship teams using ticketing integrations.
Run reconciliation jobs between catalog metadata and source system system tables.

Module 6: Managing Metadata Lineage and Dependency Graphs

Choose between coarse-grained (table-level) and fine-grained (column-level) lineage based on impact analysis needs.
Model indirect dependencies (e.g., shared dimensions, lookup tables) in lineage graphs.
Implement incremental lineage updates to avoid full reprocessing on minor changes.
Support forward and backward traversal for impact and root cause analysis workflows.
Handle schema evolution in lineage by versioning transformation rules and mapping sets.
Integrate with data observability tools to annotate lineage with freshness and quality signals.
Optimize graph storage for sub-second query response on multi-hop traversals.
Mask sensitive nodes in lineage for non-privileged users without breaking path integrity.

Module 7: Securing and Governing Metadata Access

Implement attribute-based access control (ABAC) for metadata fields containing PII or business logic.
Enforce row-level filtering in metadata queries based on user role and data domain membership.
Encrypt metadata at rest and in transit, especially for cloud-hosted repositories.
Integrate metadata access logs with SIEM systems for compliance auditing.
Define data classification propagation rules from source to derived datasets in the catalog.
Apply retention policies to metadata records based on source data lifecycle.
Restrict write access to metadata attributes to approved stewardship roles and automated pipelines.
Validate metadata changes against governance policies using pre-commit hooks in CI/CD.

Module 8: Monitoring, Alerting, and Operational Maintenance

Instrument ingestion pipelines with metrics for latency, throughput, and error rates.
Set up alerts for metadata staleness exceeding defined freshness SLAs.
Monitor repository storage growth and plan capacity based on ingestion trends.
Automate schema migration scripts for metadata model version upgrades.
Conduct regular consistency checks between metadata and source system states.
Rotate API keys and service accounts used by ingestion connectors on a quarterly basis.
Perform failover testing for high-availability metadata repository clusters.
Document runbooks for common failure scenarios (e.g., backpressure, schema drift).

Module 9: Enabling Discovery and Consumption Workflows

Implement full-text and faceted search with relevance ranking tuned to enterprise terminology.
Expose metadata via REST and GraphQL APIs for integration with custom applications.
Generate data profile summaries (sample values, distributions) for new datasets.
Integrate with IDEs and notebooks to provide inline metadata tooltips during development.
Support bookmarking and annotation features for collaborative data exploration.
Embed metadata links in operational dashboards for contextual data understanding.
Provide export functionality for metadata subsets in standard formats (JSON, CSV, RDF).
Track metadata usage patterns to prioritize curation efforts on high-traffic assets.