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Duration 35 hours
Course Outline
Data Warehousing Fundamentals
- The purpose, key components, and overall architecture of a warehouse.
- Exploring data marts, enterprise warehouses, and lakehouse models.
- Understanding the distinction between OLTP and OLAP, and the importance of workload separation.
Dimensional Modeling Techniques
- Defining facts, dimensions, and data grain.
- Comparing star schemas with snowflake schemas.
- Managing Slowly Changing Dimensions (SCD) and their various types.
ETL and ELT Workflows
- Strategies for extracting data from OLTP systems and APIs.
- Performing transformations, data cleansing, and ensuring conformance.
- Implementing load patterns, orchestration, and managing dependencies.
Data Quality and Metadata Control
- Applying data profiling and establishing validation rules.
- Aligning master and reference data.
- Tracking lineage, maintaining catalogs, and creating documentation.
Analytics and Performance Optimization
- Utilizing cubing concepts, aggregates, and materialized views.
- Leveraging partitioning, clustering, and indexing for analytical speed.
- Managing workloads, utilizing caching, and tuning queries.
Security and Governance Frameworks
- Implementing access controls, role definitions, and row-level security.
- Addressing compliance requirements and audit trails.
- Establishing backup, recovery, and reliability protocols.
Contemporary Architectures
- Leveraging cloud data warehouses and elastic scaling.
- Incorporating streaming ingestion for near real-time analytics.
- Optimizing costs and implementing monitoring solutions.
Capstone Project: Source to Star Schema
- Translating a business process into a model of facts and dimensions.
- Constructing a complete end-to-end ETL or ELT pipeline.
- Deploying dashboards and verifying metric accuracy.
Recap and Future Directions
Requirements
- Solid comprehension of relational databases and SQL.
- Practical experience in data analysis or reporting.
- Foundational knowledge of either cloud-based or on-premises data platforms.
Target Audience
- Data analysts seeking to pivot into data warehousing roles.
- 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
