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Duration 35 hours
Course Outline
Foundations of Data Warehousing
- The purpose, key components, and underlying architecture of data warehouses.
- Data marts, enterprise warehouses, and lakehouse design patterns.
- Basic principles of OLTP vs OLAP and the importance of workload separation.
Dimensional Modelling
- Understanding facts, dimensions, and data grain.
- Comparing star schema and snowflake schema structures.
- Managing Slowly Changing Dimensions (SCDs) and their various types.
ETL and ELT Processes
- Methods for extracting data from OLTP systems and APIs.
- Data transformation, cleansing, and ensuring conformance.
- Loading patterns, workflow orchestration, and handling dependencies.
Data Quality and Metadata Management
- Applying data profiling techniques and validation rules.
- Aligning master and reference data across systems.
- Managing data lineage, maintaining catalogs, and documentation.
Analytics and Performance
- Concepts of data cubing, aggregation, and materialised views.
- Strategies for partitioning, clustering, and indexing to boost analytics.
- Managing workloads, leveraging caching, and tuning queries.
Security and Governance
- Implementing access controls, role-based permissions, and row-level security.
- Addressing compliance requirements and auditing practices.
- Establishing backup, recovery, and reliability protocols.
Modern Architectures
- Leveraging cloud data warehouses and elastic scaling.
- Streaming data ingestion and enabling near real-time analytics.
- Optimising costs and monitoring system performance.
Capstone: From Source to Star Schema
- Modelling a specific business process into facts and dimensions.
- Constructing a complete end-to-end ETL or ELT workflow.
- Publishing dashboards and validating key metrics.
Summary and Next Steps
Requirements
- A solid grasp of relational databases and SQL.
- Practical experience in data analysis or reporting.
- Familiarity with either cloud-based or on-premises data platforms.
Target Audience
- Data analysts aiming to specialise in data warehousing.
- 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
