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Duration 35 hours
Course Outline
Data Warehousing Fundamentals
- The role of warehouses, core components, and architectural designs.
- Data marts, enterprise warehouses, and lakehouse architectures.
- The distinction between OLTP and OLAP and the importance of workload isolation.
Dimensional Modeling
- Understanding facts, dimensions, and data grain.
- Comparing star and snowflake schema structures.
- Managing Slowly Changing Dimensions (SCD) types and strategies.
ETL and ELT Workflows
- Strategies for extracting data from OLTP systems and APIs.
- Applying transformations, data cleansing, and conformance rules.
- Defining load patterns, orchestration, and handling dependencies.
Data Quality and Metadata Governance
- Implementing data profiling and validation rules.
- Aligning master and reference data.
- Managing lineage, catalogs, and documentation.
Analytics and Performance Optimization
- Leveraging cubes, aggregates, and materialized views.
- Utilizing partitioning, clustering, and indexing for analytics.
- Managing workloads, caching strategies, and query optimization.
Security and Governance
- Configuring access controls, roles, and row-level security.
- Addressing compliance requirements and audit trails.
- Establishing backup, recovery, and reliability protocols.
Modern Architectures
- Cloud data warehouses and elastic scaling.
- Streaming ingestion for near real-time analytics.
- Cost optimization and system monitoring.
Capstone Project: From Source to Star Schema
- Translating business processes into fact and dimension models.
- Constructing a complete ETL or ELT workflow.
- Deploying dashboards and validating key metrics.
Course Wrap-up and Path Forward
Requirements
- Proficiency in relational databases and SQL.
- Practical experience in data analysis or reporting.
- Foundational knowledge of cloud-based or on-premises data platforms.
Target Audience
- Data analysts looking to advance into data warehousing roles.
- BI developers and ETL engineers.
- Data architects and 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
