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Duration 35 hours
Course Outline
Core Principles of Data Warehousing
- Defining the purpose, key components, and overall architecture of warehouses
- Exploring data marts, enterprise warehouses, and lakehouse patterns
- Understanding OLTP vs OLAP distinctions and workload separation strategies
Dimensional Modeling Techniques
- Concepts of facts, dimensions, and data grain
- Comparing star schema and snowflake schema designs
- Managing Slowly Changing Dimensions (SCD) types and implementations
ETL and ELT Workflows
- Techniques for extracting data from OLTP sources and APIs
- Applying transformations, data cleansing, and conformance rules
- Establishing load patterns, orchestration logic, and dependency control
Data Quality and Metadata Governance
- Implementing data profiling and validation protocols
- Aligning master and reference data standards
- Managing lineage, catalogs, and comprehensive documentation
Analytics and Performance Optimization
- Utilizing cubing concepts, aggregates, and materialized views
- Applying partitioning, clustering, and indexing for analytical speed
- Managing workloads, caching mechanisms, and query performance tuning
Security and Governance Frameworks
- Configuring access controls, roles, and row-level security policies
- Addressing compliance requirements and audit trails
- Establishing backup, recovery, and high-availability practices
Contemporary Architectures
- Leveraging cloud data warehouses and elastic scaling capabilities
- Integrating streaming ingestion for near real-time analytics
- Strategies for cost optimization and continuous monitoring
Capstone Project: Source to Star Schema
- Modeling a specific business process into facts and dimensions
- Developing a complete end-to-end ETL or ELT pipeline
- Deploying dashboards and verifying metric accuracy
Course Summary and Path Forward
Requirements
- A solid grasp of relational database systems and SQL
- Practical experience in data analysis or reporting
- Basic knowledge of cloud-based or on-premises data infrastructure
Target Audience
- Data analysts looking to transition into data warehousing roles
- 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
