Data Management and Warehousing
Description
Comprehensive data management strategies including data modeling, ETL processes, data warehousing, data lakes, and master data management for big data environments.
Learning Objectives
Students will design and implement enterprise data architectures, create efficient ETL and ELT processes for big data, understand data modeling concepts for structured and unstructured data, implement data quality and governance frameworks, manage master data across organizations, and architect modern data lake and data warehouse solutions that support advanced analytics and business intelligence.
Topics (9)
Advanced data modeling techniques adapted for big data including traditional dimensional modeling and modern approaches for NoSQL and distributed data systems.
Data integration processes for combining data from multiple sources including batch and real-time processing, data transformation, and quality validation.
Modern data lake architectures for storing and processing diverse data types with focus on scalability, governance, and analytics enablement.
Contemporary data warehousing approaches using cloud platforms and modern architectures optimized for big data analytics and business intelligence.
Enterprise-wide data management practices for maintaining consistent and authoritative master data across multiple systems and business processes.
Comprehensive metadata management including technical, business, and operational metadata for enabling data governance and self-service analytics.
Data lineage and impact analysis techniques for understanding data flow, dependencies, and changes across complex big data environments.
Comprehensive data lifecycle management including archival strategies, retention policies, and automated data tiering for cost-effective big data storage.
Systematic approaches to measuring, monitoring, and improving data quality across big data environments including automated quality assurance processes.