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Duration 35 hours
Course Outline
Data Warehousing Essentials
- The purpose, key components, and structure of a warehouse.
- Data marts, enterprise warehouses, and lakehouse architectures.
- Core differences between OLTP and OLAP and separating workloads.
Dimensional Modeling Techniques
- Defining facts, dimensions, and data grain.
- Comparing star and snowflake schemas.
- Managing Slowly Changing Dimensions (SCD) and their types.
ETL and ELT Methodologies
- Strategies for extracting data from OLTP systems and APIs.
- Data transformation, cleansing, and ensuring conformance.
- Loading patterns, orchestration, and managing dependencies.
Data Quality and Metadata Stewardship
- Profiling data and establishing validation rules.
- Aligning master data and reference data.
- Tracking lineage, maintaining catalogs, and documenting processes.
Analytics Optimization and Performance
- Concepts of cubing, aggregation, and using materialized views.
- Implementing partitioning, clustering, and indexing for analytics.
- Managing workloads, leveraging caching, and tuning queries.
Security Frameworks and Governance
- Controlling access, defining roles, and applying row-level security.
- Addressing compliance requirements and auditing.
- Ensuring backup, recovery, and system reliability.
Contemporary Architectures
- Cloud-based data warehouses and elastic scaling.
- Streaming data ingestion and near real-time analytics.
- Optimizing costs and monitoring performance.
Capstone Project: From Source to Star Schema
- Modeling business processes into facts and dimensions.
- Developing a complete end-to-end ETL or ELT workflow.
- Creating dashboards and verifying metric accuracy.
Summary and Future Directions
Requirements
- Familiarity with relational databases and SQL.
- Prior experience in data analysis or reporting.
- Basic knowledge of cloud-based or on-premises data platforms.
Target Audience
- Data analysts looking to specialize in data warehousing.
- 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
