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Course Outline
Foundations of Data Warehousing
- Purpose, key components, and architectural design of warehouses.
- Data marts, enterprise warehouses, and lakehouse patterns.
- Fundamentals of OLTP versus OLAP and workload separation.
Dimensional Modeling
- Understanding facts, dimensions, and grain.
- Comparing star schema and snowflake schema approaches.
- Managing Slowly Changing Dimensions (SCDs) types and techniques.
ETL and ELT Processes
- Strategies for extracting data from OLTP systems and APIs.
- Data transformation, cleansing, and ensuring conformance.
- Loading patterns, orchestration, and dependency management.
Data Quality and Metadata Management
- Data profiling and establishing validation rules.
- Aligning master and reference data.
- Data lineage, catalogs, and documentation practices.
Analytics and Performance Optimization
- Concepts of cubing, aggregates, and materialized views.
- Utilizing partitioning, clustering, and indexing for analytics.
- Workload management, caching strategies, and query tuning.
Security and Governance
- Access control mechanisms, roles, and row-level security.
- Compliance requirements and auditing processes.
- Backup, recovery procedures, and reliability best practices.
Modern Architectures
- Cloud data warehouses and scalability elasticity.
- Streaming ingestion and near real-time analytics capabilities.
- Cost optimization techniques and monitoring strategies.
Capstone Project: From Source to Star Schema
- Modelling business processes into facts and dimensions.
- Constructing an end-to-end ETL or ELT workflow.
- Publishing dashboards and validating key metrics.
Course Summary and Next Steps
Requirements
- Foundational knowledge of relational databases and SQL.
- Prior experience in data analysis or reporting.
- Basic familiarity with either cloud-based or on-premises data platforms.
Target Audience
- Data analysts looking to transition into data warehousing.
- Business Intelligence (BI) developers and ETL engineers.
- Data architects and team leads.
35 Hours
Testimonials (1)
Hands on exercises. Class should have been 5 days, but the 3 days helped to clear up a lot of questions that I had from working with NiFi already
