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Duration 35 hours
Course Outline
Data Warehousing Fundamentals
- Understanding the purpose, key components, and overall architecture.
- Exploring data marts, enterprise warehouses, and lakehouse patterns.
- Distinguishing between OLTP and OLAP, and separating workloads effectively.
Dimensional Modeling Techniques
- Defining facts, dimensions, and data grain.
- Comparing star schema and snowflake schema structures.
- Handling different types of Slowly Changing Dimensions (SCD).
ETL and ELT Methodologies
- Developing extraction strategies from OLTP systems and APIs.
- Applying transformations, data cleansing, and conformance rules.
- Managing load patterns, orchestration, and dependencies.
Data Quality and Metadata Control
- Implementing data profiling and validation rules.
- Aligning master data and reference data.
- Managing lineage, data catalogs, and documentation.
Analytics and Performance Optimization
- Utilizing cubing concepts, aggregates, and materialized views.
- Applying partitioning, clustering, and indexing for analytics.
- Managing workloads, leveraging caching, and tuning queries.
Security and Governance Frameworks
- Configuring access control, roles, and row-level security.
- Addressing compliance requirements and auditing processes.
- Establishing backup, recovery, and reliability protocols.
Contemporary Architectures
- Leveraging cloud data warehouses and their elastic capabilities.
- Implementing streaming ingestion for near real-time analytics.
- Optimizing costs and monitoring system health.
Capstone Project: From Source to Star Schema
- Translating business processes into facts and dimensions.
- Constructing a complete end-to-end ETL or ELT workflow.
- Deploying dashboards and verifying metric accuracy.
Wrap-up and Recommended Next Steps
Requirements
- Solid comprehension of relational databases and SQL language.
- Practical experience in data analysis or reporting.
- Foundational familiarity with cloud-based or on-premises data platforms.
Intended Audience
- Data analysts seeking to specialize in data warehousing.
- BI developers and ETL engineers.
- Data architects and 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
