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Duration 35 hours
Course Outline
Data Warehousing Fundamentals
- The role, essential components, and overall architecture of the warehouse.
- Understanding data marts, enterprise warehouses, and lakehouse patterns.
- Core differences between OLTP and OLAP, and the importance of workload separation.
Dimensional Modeling
- Defining facts, dimensions, and the concept of grain.
- Comparative analysis of star schema versus snowflake schema structures.
- Managing Slowly Changing Dimensions and understanding their various types.
ETL and ELT Workflows
- Strategies for extracting data from OLTP systems and APIs.
- Handling transformations, data cleansing, and ensuring conformance.
- Applying load patterns, orchestrating processes, and managing dependencies.
Data Quality and Metadata Management
- Implementing data profiling and establishing validation rules.
- Aligning master data and reference data.
- Managing lineage, maintaining data catalogs, and documenting assets.
Analytics and Performance Optimization
- Concepts of cubing, creating aggregates, and utilizing materialized views.
- Optimizing analytics through partitioning, clustering, and indexing.
- Managing workloads, implementing caching, and fine-tuning queries.
Security and Governance
- Implementing access controls, defining roles, and enforcing row-level security.
- Addressing compliance requirements and establishing auditing protocols.
- Best practices for backup, recovery, and system reliability.
Modern Architectures
- Leveraging cloud data warehouses and their elastic capabilities.
- Enabling streaming ingestion for near real-time analytics.
- Strategies for cost optimization and continuous monitoring.
Capstone Project: From Source to Star Schema
- Translating a business process into structured facts and dimensions.
- Developing a complete end-to-end ETL or ELT workflow.
- Creating dashboards and verifying the accuracy of key metrics.
Summary and Recommended Next Steps
Requirements
- Solid knowledge of relational databases and SQL.
- Prior experience in data analysis or business reporting.
- Foundational familiarity with cloud-based or on-premises data platforms.
Target Audience
- Data analysts seeking to expand their expertise into data warehousing.
- BI developers and ETL engineers.
- Data architects and technical team leads.
Testimonials (3)
Data governance
Ignacio Jimenez - Contraloria General de la Republica
Course - Data Warehousing: Concepts, Design, and Implementation
Machine Translated
information security
Patricia Quezada - Contraloria General de la Republica
Course - Data Warehousing: Concepts, Design, and Implementation
Machine Translated
The learning was more conceptual: better understanding what Data Warehousing entails, the processes involved, the differences between OLTP and OLAP databases, between local, cloud, and hybrid storage, as well as mentioning some open-source tools for application.
Edgar Luis Vasquez Arauz - Contraloria General de la Republica
Course - Data Warehousing: Concepts, Design, and Implementation
Machine Translated
