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Duration 35 hours
Course Outline
Data Warehousing Essentials
- The role, structure, and architectural components of a warehouse
- Data marts, enterprise warehouses, and lakehouse architectures
- Basics of OLTP versus OLAP and the importance of workload isolation
Dimensional Modeling Techniques
- Understanding facts, dimensions, and data grain
- Comparing star schemas against snowflake schemas
- Managing Slowly Changing Dimensions (SCD) types and implementations
ETL and ELT Workflows
- Methods for extracting data from OLTP systems and APIs
- Data transformation, cleansing, and conformance strategies
- Loading patterns, orchestration, and managing dependencies
Data Quality and Metadata Control
- Data profiling techniques and validation rules
- Aligning master and reference data
- Data lineage, cataloging, and documentation practices
Analytics and Performance Tuning
- Concepts of cubing, aggregation, and materialized views
- Optimizing analytics through partitioning, clustering, and indexing
- Workload management, caching strategies, and query optimization
Security and Governance Frameworks
- Access controls, role management, and row-level security
- Compliance requirements and audit trails
- Backup procedures, disaster recovery, and reliability standards
Contemporary Architectures
- Cloud data warehouses and elastic scaling capabilities
- Streaming ingestion for near real-time analytics
- Cost efficiency and system monitoring
Capstone Project: Source to Star Schema
- Translating business processes into facts and dimensions
- Developing a complete end-to-end ETL or ELT workflow
- Deploying dashboards and verifying metric accuracy
Course Recap and Recommended Next Steps
Requirements
- Solid grasp of relational databases and SQL
- Prior exposure to data analysis or reporting workflows
- Fundamental knowledge of cloud-based or on-premise data platforms
Target Audience
- Data analysts moving into data warehousing roles
- BI developers and ETL specialists
- Data architects and technical 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
