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Duration 35 hours
Course Outline
Fundamental Principles of Data Warehousing
- The role, constituent elements, and structural design of data warehouses.
- Exploring data marts, enterprise warehouses, and lakehouse architectural patterns.
- Core differences between OLTP and OLAP and the importance of workload isolation.
Dimensional Modeling Techniques
- Understanding facts, dimensions, and data grain.
- Comparative analysis of star versus snowflake schema structures.
- Managing Slowly Changing Dimensions (SCD) types and their implementation.
ETL and ELT Workflow Management
- Methods for extracting data from OLTP systems and APIs.
- Applying transformations, data cleansing, and ensuring data conformance.
- Establishing load patterns, orchestration strategies, and managing dependencies.
Data Quality and Metadata Stewardship
- Implementing data profiling techniques and validation rules.
- Aligning master data with reference data for consistency.
- Tracking lineage, managing catalogs, and maintaining comprehensive documentation.
Analytical Performance and Optimization
- Utilizing cubing concepts, aggregates, and materialized views.
- Enhancing performance through partitioning, clustering, and strategic indexing.
- Optimizing workload management, caching mechanisms, and query execution.
Security Frameworks and Governance
- Implementing access controls, role-based permissions, and row-level security.
- Addressing compliance requirements and establishing audit trails.
- Establishing practices for backup, disaster recovery, and system reliability.
Contemporary Data Architectures
- Leveraging cloud data warehouses and their elastic capabilities.
- Integrating streaming ingestion for near real-time analytical insights.
- Strategies for cost optimization and continuous system monitoring.
Capstone Project: From Data Source to Star Schema
- Translating a specific business process into a structured fact and dimension model.
- Constructing a comprehensive end-to-end ETL or ELT data workflow.
- Deploying dashboards and verifying the accuracy of key metrics.
Course Summary and Recommended Next Steps
Requirements
- Proficiency in relational databases and SQL.
- Practical experience in data analysis or reporting activities.
- Foundational knowledge of cloud-based or on-premises data platforms.
Target Audience
- Data analysts seeking to expand their skill set into data warehousing.
- Business Intelligence developers and ETL engineers.
- 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
