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Duration 35 hours
Course Outline
Core Data Warehousing Principles
- The role, components, and architecture of the data warehouse.
- Data marts, enterprise warehouses, and lakehouse patterns.
- Fundamentals of OLTP vs OLAP and the importance of workload separation.
Dimensional Modelling Techniques
- Understanding facts, dimensions, and data grain.
- Comparing star schema and snowflake schema approaches.
- Managing Slowly Changing Dimensions (SCDs) and their various types.
ETL and ELT Processes
- Extraction strategies from OLTP systems and APIs.
- Data transformations, cleansing, and ensuring conformance.
- Load patterns, orchestration, and managing dependencies.
Data Quality and Metadata Management
- Implementing data profiling and validation rules.
- Aligning master and reference data.
- Establishing lineage, data catalogs, and comprehensive documentation.
Analytics and Performance Optimisation
- Cubing concepts, aggregates, and materialized views.
- Applying partitioning, clustering, and indexing for better analytics.
- Workload management, caching strategies, and query tuning.
Security and Governance
- Access controls, role management, and row-level security.
- Addressing compliance considerations and auditing requirements.
- Best practices for backup, recovery, and system reliability.
Modern Data Architectures
- Cloud data warehouses and the benefits of elasticity.
- Streaming ingestion and near real-time analytics capabilities.
- Strategies for cost optimisation and continuous monitoring.
Capstone: From Source to Star Schema
- Modelling a business process into specific facts and dimensions.
- Developing a complete end-to-end ETL or ELT workflow.
- Publishing dashboards and validating key performance metrics.
Summary and Next Steps
Requirements
- A solid grasp of relational databases and SQL.
- Prior experience in data analysis or reporting.
- Foundational familiarity with cloud or on-premises data platforms.
Target Audience
- Data analysts moving into data warehousing roles.
- BI developers and ETL engineers.
- Data architects and technical team leads.
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
