Cursusaanbod

Foundations of Data Warehousing

  • Warehouse purpose, components, and architecture
  • Data marts, enterprise warehouses, and lakehouse patterns
  • OLTP vs OLAP fundamentals and workload separation

Dimensional Modeling

  • Facts, dimensions, and grain
  • Star schema vs snowflake schema
  • Slowly Changing Dimensions types and handling

ETL and ELT Processes

  • Extraction strategies from OLTP and APIs
  • Transformations, data cleansing, and conformance
  • Load patterns, orchestration, and dependency management

Data Quality and Metadata Management

  • Data profiling and validation rules
  • Master and reference data alignment
  • Lineage, catalogs, and documentation

Analytics and Performance

  • Cubing concepts, aggregates, and materialized views
  • Partitioning, clustering, and indexing for analytics
  • Workload management, caching, and query tuning

Security and Governance

  • Access control, roles, and row-level security
  • Compliance considerations and auditing
  • Backup, recovery, and reliability practices

Modern Architectures

  • Cloud data warehouses and elasticity
  • Streaming ingestion and near real-time analytics
  • Cost optimization and monitoring

Capstone: From Source to Star Schema

  • Modeling a business process into facts and dimensions
  • Building an end-to-end ETL or ELT workflow
  • Publishing dashboards and validating metrics

Summary and Next Steps

Vereisten

  • An understanding of relational databases and SQL
  • Experience with data analysis or reporting
  • Basic familiarity with cloud or on-premises data platforms

Audience

  • Data analysts transitioning to data warehousing
  • BI developers and ETL engineers
  • Data architects and team leads
 35 Uren

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