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Duration 35 hours
Course Outline
Data Warehousing Foundations
- Core objectives, key components, and structural design.
- Distinguishing between data marts, enterprise warehouses, and lakehouse patterns.
- Understanding OLTP versus OLAP paradigms and workload isolation.
Dimensional Modeling Techniques
- Defining facts, dimensions, and data grain.
- Comparing star and snowflake schema structures.
- Managing various types of Slowly Changing Dimensions.
ETL and ELT Workflow Management
- Strategies for extracting data from OLTP sources and APIs.
- Executing transformations, data cleansing, and conformance checks.
- Optimizing load patterns, orchestration, and dependency handling.
Data Quality and Metadata Control
- Implementing data profiling and validation protocols.
- Aligning master and reference data structures.
- Establishing lineage, catalogs, and comprehensive documentation.
Analytical Performance and Optimization
- Leveraging cubing concepts, aggregates, and materialized views.
- Applying partitioning, clustering, and indexing strategies for analytics.
- Managing workloads, caching mechanisms, and query optimization.
Security Frameworks and Governance
- Configuring access controls, roles, and row-level security.
- Addressing compliance requirements and audit trails.
- Establishing backup, recovery, and reliability standards.
Contemporary Architectures
- Utilizing cloud data warehouses for elastic scaling.
- Enabling streaming ingestion for near real-time insights.
- Refining cost structures and monitoring usage.
Capstone Project: Source to Schema
- Translating business processes into facts and dimensions.
- Constructing a complete ETL or ELT workflow.
- Deploying dashboards and verifying metric accuracy.
Recap and Future Recommendations
Requirements
- Proficiency in relational databases and SQL.
- Practical experience in data analysis or reporting functions.
- Foundational knowledge of cloud-based or on-premises data infrastructure.
Intended Audience
- Data analysts advancing into data warehousing roles.
- BI specialists and ETL engineering professionals.
- Data architects and senior 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
