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Course Outline
The Basics of Data Warehousing
- The role, key components, and structure of a data warehouse.
- Data marts, enterprise-level warehouses, and lakehouse strategies.
- Key differences between OLTP and OLAP and managing workload separation.
Dimensional Modeling Techniques
- Understanding facts, dimensions, and data grain.
- Comparing star schemas with snowflake schemas.
- Managing Slowly Changing Dimensions (SCD) types and implementation.
ETL and ELT Workflows
- Techniques for extracting data from OLTP systems and APIs.
- Data transformation, cleansing, and ensuring conformance.
- Loading strategies, orchestration, and handling dependencies.
Ensuring Data Quality and Managing Metadata
- Applying data profiling and setting validation rules.
- Aligning master and reference data.
- Tracking lineage, maintaining catalogs, and documenting data.
Analytics and System Performance
- Concepts of cubing, aggregates, and using materialized views.
- Optimizing analytics through partitioning, clustering, and indexing.
- Managing workloads, utilizing caching, and tuning queries.
Security Frameworks and Governance
- Implementing access controls, defining roles, and row-level security.
- Addressing compliance requirements and conducting audits.
- Establishing best practices for backup, recovery, and reliability.
Contemporary Data Architectures
- Utilizing cloud data warehouses and leveraging elasticity.
- Ingesting streaming data for near real-time analytics.
- Strategies for cost efficiency and system monitoring.
Capstone Project: From Source Data to Star Schema
- Translating business processes into facts and dimensions.
- Creating a complete end-to-end ETL or ELT workflow.
- Deploying dashboards and verifying metric accuracy.
Recap and Future Directions
Requirements
- A solid grasp of relational databases and SQL.
- Practical experience in data analysis or reporting.
- Foundational knowledge of cloud-based or on-premises data platforms.
Target Audience
- Data analysts aiming to specialise in data warehousing.
- BI developers and ETL engineers.
- Data architects and technical team leads.
35 Hours
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