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Duration 35 hours
Course Outline
Core Data Warehousing Principles
- The purpose, key components, and overall architecture of warehouses.
- Data marts, enterprise-wide warehouses, and modern lakehouse patterns.
- The distinction between OLTP and OLAP, including workload isolation strategies.
Dimensional Modeling Strategies
- Understanding facts, dimensions, and data grain.
- Comparing star and snowflake schema designs.
- Managing Slowly Changing Dimensions (SCD) and their various types.
ETL and ELT Workflows
- Strategies for extracting data from OLTP systems and APIs.
- Applying transformations, data cleansing, and ensuring conformance.
- Defining load patterns, orchestrating flows, and managing dependencies.
Data Quality and Metadata Control
- Implementing data profiling and establishing validation rules.
- Aligning master and reference data across the organization.
- Tracking lineage, managing catalogs, and maintaining documentation.
Analytics and Performance Optimization
- Leveraging cubing, aggregates, and materialized views.
- Utilizing partitioning, clustering, and indexing for analytical speed.
- Managing workloads, implementing caching, and fine-tuning queries.
Security and Governance Frameworks
- Configuring access controls, roles, and row-level security.
- Addressing compliance requirements and audit trails.
- Establishing backup, recovery, and high-availability protocols.
Modern Architectural Trends
- The role of cloud data warehouses and elastic scaling.
- Enabling streaming ingestion and near real-time analytics.
- Strategies for cost optimization and continuous monitoring.
Capstone Project: Source to Star Schema
- Modeling a specific business process into facts and dimensions.
- Creating a complete end-to-end ETL or ELT workflow.
- Publishing dashboards and verifying the accuracy of metrics.
Course Summary and Future Pathways
Requirements
- A solid grasp of relational databases and SQL syntax.
- Prior experience in data analysis or business reporting.
- Foundational knowledge of cloud-based or on-premises data infrastructure.
Target Audience
- Data analysts seeking to expand into data warehousing roles.
- BI developers and ETL engineering professionals.
- Data architects and technical 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
