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Duration 35 hours
Course Outline
Data Warehousing Fundamentals
- Objectives, core components, and architectural patterns
- Data marts, enterprise-wide warehouses, and lakehouse paradigms
- OLTP vs. OLAP basics and workload segregation
Dimensional Modeling Techniques
- Facts, dimensions, and data grain
- Comparative analysis of star and snowflake schemas
- Managing Slowly Changing Dimensions (SCDs) and their types
ETL and ELT Methodologies
- Data extraction techniques from OLTP systems and APIs
- Transformation logic, data cleaning, and conformance checks
- Loading strategies, orchestration, and dependency tracking
Data Integrity and Metadata Control
- Profiling techniques and validation rule implementation
- Aligning master and reference data sets
- Data lineage, cataloging, and documentation standards
Analytical Performance Optimization
- OLAP cubing, aggregation strategies, and materialized views
- Partitioning, clustering, and indexing for analytical speed
- Workload management, caching mechanisms, and query tuning
Security Frameworks and Governance
- Access controls, role-based permissions, and row-level security
- Regulatory compliance and audit trails
- Backup, disaster recovery, and high-availability practices
Contemporary Data Architectures
- Cloud-native data warehouses and elastic scaling
- Streaming ingestion and near real-time analytics
- Cost-efficiency monitoring and resource optimization
Capstone Project: Source to Star Schema
- Translating business processes into fact and dimension models
- Constructing a complete end-to-end ETL or ELT workflow
- Deploying dashboards and verifying metric accuracy
Course Recap and Future Pathways
Requirements
- Proficiency in relational database structures and SQL
- Practical experience in data analysis or report generation
- Foundational knowledge of cloud or on-premises data infrastructure
Target Audience
- Data analysts expanding their scope into data warehousing
- BI developers and ETL specialists
- Data architects and engineering team leaders
Testimonials (2)
A journey through the Spark world: a very intense course. DSL, spark sql, partitioning vs bucketing for me.
Georgiana Elisabeta
Course - Apache Spark Fundamentals
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
