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Duration 35 hours
Course Outline
Fundamental Principles of Data Warehousing
- Defining the purpose, key components, and architectural design of warehouses.
- Examining data marts, enterprise-level warehouses, and lakehouse architectures.
- Understanding the distinction between OLTP and OLAP fundamentals and the importance of workload separation.
Dimensional Modeling Techniques
- Identifying facts, dimensions, and data grain.
- Comparing star schema and snowflake schema designs.
- Managing Slowly Changing Dimensions (SCD) types and update strategies.
ETL and ELT Workflow Management
- Developing extraction strategies from OLTP systems and APIs.
- Executing transformations, data cleansing, and ensuring conformance.
- Implementing load patterns, orchestration, and dependency tracking.
Data Quality and Metadata Governance
- Applying data profiling techniques and establishing validation rules.
- Aligning master and reference data for consistency.
- Maintaining lineage, catalogs, and comprehensive documentation.
Analytical Performance Optimization
- Leveraging cubing concepts, aggregates, and materialized views.
- Utilizing partitioning, clustering, and indexing for analytical speed.
- Managing workloads, optimizing caching, and tuning queries.
Security Protocols and Governance
- Enforcing access controls, defining roles, and implementing row-level security.
- Addressing compliance requirements and conducting audits.
- Establishing backup, recovery, and reliability standards.
Contemporary Architectures
- Harnessing cloud data warehouses and elastic scalability.
- Integrating streaming ingestion for near real-time analytics.
- Optimizing costs and implementing continuous monitoring.
Capstone Project: End-to-End Implementation
- Translating a business process into a structured set of facts and dimensions.
- Constructing a complete ETL or ELT workflow from source to destination.
- Deploying dashboards and verifying metric accuracy.
Course Recap and Future Pathways
Requirements
- Solid grasp of relational database systems and SQL queries.
- Prior experience in data analysis or reporting tasks.
- Foundational knowledge of cloud-based or on-premises data platforms.
Target Audience
- Data analysts expanding their expertise into data warehousing.
- Business Intelligence (BI) developers and ETL engineers.
- Data architects and technical 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
