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Duration 35 hours
Course Outline
Data Warehousing Fundamentals
- Defining the purpose, key components, and overall architecture
- Exploring data marts, enterprise warehouses, and lakehouse patterns
- Distinguishing OLTP from OLAP and managing workload separation
Dimensional Modeling Techniques
- Understanding facts, dimensions, and data grain
- Comparing star schema versus snowflake schema designs
- Managing Slowly Changing Dimensions (SCDs) and their types
ETL and ELT Workflows
- Strategies for extracting data from OLTP systems and APIs
- Implementing transformations, data cleansing, and conformance checks
- Defining load patterns, orchestration logic, and dependency management
Data Quality and Metadata Governance
- Applying data profiling techniques and validation rules
- Aligning master and reference data
- Documenting lineage, catalogs, and technical specifications
Analytics Optimization and Performance
- Leveraging cubing concepts, aggregates, and materialized views
- Optimizing via partitioning, clustering, and indexing strategies
- Managing workloads, implementing caching, and tuning queries
Security and Compliance Governance
- Implementing access controls, roles, and row-level security
- Addressing compliance requirements and auditing practices
- Establishing backup, recovery, and high-reliability protocols
Contemporary Architectures
- Utilizing cloud data warehouses and elastic scaling
- Integrating streaming ingestion for near real-time analytics
- Optimizing costs and implementing monitoring solutions
Capstone Project: Source to Star Schema
- Modeling a specific business process into facts and dimensions
- Constructing a complete end-to-end ETL or ELT workflow
- Deploying dashboards and verifying metric accuracy
Recap and Recommended Next Steps
Requirements
- Solid grasp of relational database structures and SQL syntax
- Prior exposure to data analysis or reporting tasks
- Foundational knowledge of cloud-based or on-premises data platforms
Intended Audience
- Data analysts seeking to pivot 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
