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Duration 35 hours
Course Outline
Data Warehousing Foundations
- Objectives, key components, and overall architecture of warehouses.
- Concepts of data marts, enterprise warehouses, and lakehouse models.
- Core differences between OLTP and OLAP and strategies for workload separation.
Dimensional Modeling
- Understanding facts, dimensions, and data grain.
- Comparing star and snowflake schema structures.
- Managing Slowly Changing Dimensions and their various types.
ETL and ELT Processes
- Extraction techniques from OLTP systems and APIs.
- Applying transformations, cleansing data, and ensuring conformance.
- Load strategies, orchestration, and managing dependencies.
Data Quality and Metadata Management
- Implementing data profiling and validation rules.
- Aligning master and reference data.
- Tracking lineage, maintaining catalogs, and ensuring documentation accuracy.
Analytics and Performance
- Utilizing cubing concepts, aggregates, and materialized views.
- Optimizing analytics through partitioning, clustering, and indexing.
- Managing workloads, leveraging caching, and tuning queries.
Security and Governance
- Implementing access controls, role definitions, and row-level security.
- Addressing compliance requirements and audit trails.
- Ensuring reliability through backup and recovery practices.
Modern Architectures
- Leveraging cloud data warehouses and elastic scaling.
- Enabling streaming ingestion for near real-time analytics.
- Optimizing costs and monitoring system health.
Capstone: From Source to Star Schema
- Translating business processes into facts and dimensions.
- Constructing a complete end-to-end ETL or ELT workflow.
- Publishing dashboards and verifying metric accuracy.
Wrap-up and Future Pathways
Requirements
- Proficiency in relational databases and SQL.
- Practical experience in data analysis or reporting.
- Fundamental knowledge of cloud or on-premises data platforms.
Target Audience
- Data analysts aiming 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
