Modern Data Warehouse Architecture

Course Overview

This course covers dimensional modeling techniques including star schemas, slowly changing dimensions, and data vault methodology for building robust analytics foundations. Participants learn to design warehouse structures that support efficient querying while maintaining historical accuracy across business dimensions.

Students work with leading cloud platforms including Snowflake, BigQuery, and Redshift, understanding their specific optimization patterns and architectural considerations. The curriculum addresses data lakehouse concepts using Delta Lake, enabling unified batch and streaming analytics with ACID transactions.

Hands-on labs focus on building ELT pipelines with dbt, implementing data governance frameworks, and creating semantic layers for self-service analytics. Projects include migrating legacy warehouses, designing real-time data marts, and establishing data quality monitoring systems.

Technical sessions cover query optimization strategies, materialized view management, and partition design for performance tuning. Participants develop skills in capacity planning, cost optimization, and building analytics platforms that scale with organizational needs.

Expected Outcomes

Architecture Design

Create warehouse schemas that balance query performance with maintenance complexity using proven dimensional modeling patterns

Cloud Platform Expertise

Implement warehouses on Snowflake and BigQuery with platform-specific features including clustering, partitioning, and caching strategies

Data Governance

Establish metadata management, lineage tracking, and access control frameworks for enterprise data platforms

Performance Optimization

Tune queries and warehouse configurations to reduce costs while maintaining acceptable response times for analytical workloads

Technical Stack

Cloud Platforms

Snowflake architecture
Google BigQuery
Amazon Redshift
Azure Synapse Analytics

Modeling Tools

dbt transformations
Delta Lake integration
Data vault patterns
Kimball methodology

Analytics Layer

Semantic modeling
BI tool integration
Query optimization
Materialized views

Development Approach

Students gain experience with infrastructure-as-code using Terraform for warehouse provisioning and configuration management. The curriculum emphasizes version-controlled schemas, automated testing of transformations, and documentation practices for maintaining complex data models.

Projects incorporate CI/CD workflows for deploying warehouse changes with rollback capabilities. Participants learn monitoring strategies for tracking query performance, storage costs, and data freshness metrics across production environments.

Who Should Attend

Ideal For

Data architects designing analytics infrastructure
BI engineers modernizing reporting systems
Database administrators moving to cloud platforms
Analytics leads building self-service platforms

Prerequisites

Strong SQL skills including joins and aggregations
Understanding of relational database concepts
Experience with data modeling or ETL processes
Basic knowledge of business intelligence concepts

Skill Development Tracking

Architecture Assessments

Progress evaluations through design exercises requiring schema modeling decisions and justifications. Students receive feedback on architectural choices, considering factors like query patterns, data volumes, and business requirements. Projects build from simple star schemas to complex data vault implementations.

5

Modeling assignments

4

Platform labs

1

Migration project

Implementation Metrics

Track development through warehouse performance indicators including query response times, storage efficiency, and cost optimization achievements. Students benchmark their designs against industry patterns and receive guidance on refinement strategies. The curriculum emphasizes iterative improvement based onge patterns.

Query performance improvements
Cost reduction strategies
Schema design quality scores
Documentation completeness

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