Description

This curriculum spans the design and operationalization of big data governance across distributed data environments, comparable in scope to a multi-phase advisory engagement addressing strategy, policy automation, and cross-functional coordination in large-scale data platforms.

Module 1: Defining Big Data Governance Strategy

Selecting data domains for initial governance focus based on regulatory exposure, business impact, and data volume growth trends
Aligning big data governance objectives with enterprise data strategy while accounting for unstructured and semi-structured data sources
Deciding whether to extend existing governance frameworks or build a parallel model for big data environments
Establishing governance ownership for data lakes where data is ingested from multiple decentralized sources
Defining thresholds for data quality and metadata completeness before data is promoted to trusted zones
Integrating data governance KPIs with DevOps and data engineering performance metrics
Assessing the feasibility of enforcing governance policies in real-time streaming pipelines
Documenting data lineage requirements for machine learning features derived from raw big data sources

Module 2: Organizational Roles and Accountability Models

Assigning data stewardship responsibilities for log files, sensor data, and clickstream datasets with no clear business owner
Designing escalation paths for resolving data quality issues in shared data lake zones
Implementing role-based access controls for data scientists, analysts, and engineers in multi-tenant Hadoop or cloud environments
Creating governance review boards with representation from data engineering, compliance, and business units
Defining escalation procedures when data scientists bypass governed pipelines for exploratory analysis
Establishing accountability for metadata accuracy in self-service data catalog tools
Coordinating between central governance teams and decentralized data product owners in a data mesh architecture
Managing conflicts between data privacy requirements and data science model training needs

Module 3: Metadata Management at Scale

Automating technical metadata extraction from Spark jobs, Kafka topics, and Parquet file schemas
Implementing metadata tagging standards for transient and ephemeral datasets in streaming environments
Choosing between centralized and distributed metadata repositories for multi-cloud data lakes
Handling schema evolution in Avro or Protobuf formats and propagating changes to downstream consumers
Mapping business glossary terms to raw data fields in unstructured JSON or log data
Enforcing metadata completeness checks before allowing datasets to be published to shared zones
Integrating data catalog tools with CI/CD pipelines to capture metadata during deployment
Managing metadata retention policies for temporary datasets used in machine learning pipelines

Module 4: Data Quality in Distributed Systems

Designing data quality rules for semi-structured data where schema enforcement is relaxed
Implementing real-time anomaly detection in streaming data using statistical baselines
Defining acceptable data freshness thresholds for batch and streaming pipelines
Handling duplicate records in event-driven architectures with at-least-once delivery semantics
Measuring completeness for datasets with optional or sparse fields
Creating feedback loops from data consumers to data producers for quality issue resolution
Automating data profiling on newly ingested datasets to detect unexpected value distributions
Setting data quality SLAs for datasets used in regulatory reporting versus exploratory analytics

Module 5: Data Lineage and Provenance Tracking

Automating lineage capture from ETL workflows in Airflow, Spark, and Flink environments
Mapping transformations across multiple layers of a data lake (raw, curated, aggregated)
Handling lineage for ad hoc queries and notebooks that modify or combine governed datasets
Storing lineage data at appropriate granularity to balance performance and auditability
Integrating lineage information with data catalog tools for end-user transparency
Reconstructing data provenance for datasets that have undergone schema migrations
Supporting impact analysis for regulatory changes by tracing data elements to downstream reports
Managing lineage for machine learning models that consume features from multiple upstream sources

Module 6: Policy Enforcement and Compliance Automation

Embedding data masking rules in query engines like Presto or Spark SQL for PII fields
Implementing dynamic data access policies based on user role, data sensitivity, and location
Automating GDPR right-to-erasure requests across distributed data stores and backups
Enforcing data retention policies in object storage with lifecycle management rules
Validating data usage against consent records in customer data platforms
Integrating policy engines with data catalog tools to block unauthorized dataset access
Monitoring for policy violations in real-time using audit logs from data platforms
Handling compliance exceptions for data science sandboxes with time-bound approvals

Module 7: Data Catalog and Discovery Implementation

Configuring automated scanners to index datasets in S3, ADLS, or HDFS with appropriate frequency
Implementing search ranking algorithms that prioritize datasets with complete metadata and high usage
Integrating user feedback mechanisms to flag outdated or inaccurate catalog entries
Enabling dataset annotation features for data stewards to add business context
Managing access controls for catalog entries to prevent exposure of sensitive data descriptions
Synchronizing catalog metadata with BI tools and data science platforms
Handling catalog scalability for environments with millions of datasets and files
Implementing deprecation workflows for datasets that are no longer maintained

Module 8: Privacy and Security in Big Data Environments

Classifying data sensitivity levels for unstructured text, images, and audio files
Implementing column-level encryption for sensitive fields in Parquet and ORC files
Configuring secure access to data lakes using federated identity and short-lived credentials
Managing key rotation policies for encryption keys used across distributed storage
Enforcing network segmentation between development, staging, and production data zones
Conducting privacy impact assessments for new data ingestion pipelines
Implementing data minimization techniques in streaming pipelines to reduce retention of PII
Monitoring for unauthorized data exfiltration using access pattern anomaly detection

Module 9: Integration with Data Science and ML Workflows

Establishing governance checkpoints for feature stores used in machine learning pipelines
Tracking model training data lineage to support reproducibility and audit requirements
Implementing version control for datasets used in model development and validation
Defining data access protocols for data scientists working in isolated compute environments
Enforcing data use agreements for external datasets incorporated into training sets
Monitoring for data drift in production model inputs using statistical process control
Creating governed pathways for promoting experimental models to production
Documenting data transformations applied during feature engineering for regulatory review

Module 10: Monitoring, Auditing, and Continuous Improvement

Designing governance dashboards that track metadata completeness, policy violations, and stewardship activity
Implementing automated alerts for unauthorized schema changes or access pattern anomalies
Conducting quarterly audits of data lake permissions and access logs
Measuring time-to-resolution for data quality incidents reported through governance channels
Tracking adoption rates of governed data pipelines versus shadow IT solutions
Reviewing and updating data classification policies based on new regulatory requirements
Performing root cause analysis on recurring governance failures in data ingestion processes
Iterating governance processes based on feedback from data consumer surveys and incident reviews