Data Warehouse Specialist Course

Lesson 1 • What Is a Data Warehouse

Defines a data warehouse and contrasts it with OLTP systems. Grounds all subsequent architecture decisions in this distinction.

Lesson 2 • History and Evolution of Warehousing

Traces warehousing from early decision-support systems to modern cloud platforms. Provides context for why current architectures exist.

Lesson 3 • Core Warehousing Concepts

Introduces granularity, integration, and metadata as foundational principles. These concepts underpin every design choice covered later.

Lesson 4 • Key Roles and Stakeholders

Identifies the professionals who build, maintain, and consume a warehouse. Clarifies the specialist's responsibilities within a broader team.

Lesson 1 • Advanced Dimensional Patterns

Explores degenerate dimensions, junk dimensions, and factless fact tables. Prepares students for complex real-world modeling challenges.

Lesson 2 • Relational Modeling Refresher

Reviews normalization forms and entity-relationship diagrams as a baseline. Establishes why normalized models are often unsuitable for analytics.

Lesson 3 • Slowly Changing Dimensions

Covers SCD Types 1 through 6 for tracking historical attribute changes. Students implement the correct type based on business requirements.

Lesson 4 • Dimensional Modeling Principles

Introduces facts, dimensions, and the star schema as the primary analytical model. Students apply Kimball's four-step design process.

Lesson 1 • Loading Strategies and Patterns

Compares insert-only, upsert, and truncate-reload loading patterns. Students match loading patterns to SCD types and business refresh requirements.

Lesson 2 • ETL Pipeline Architecture

Defines the three ETL phases and their sequence within a warehouse pipeline. Establishes the structural foundation for all pipeline development work.

Lesson 3 • Data Transformation Logic

Teaches cleansing, standardization, derivation, and aggregation transformations. Students write reusable transformation rules applied across multiple pipelines.

Lesson 4 • Error Handling and Pipeline Logging

Implements reject tables, retry logic, and audit logging for production pipelines. Students build pipelines that fail gracefully and support root-cause analysis.

Lesson 1 • Set-Based Query Thinking

Shifts students from row-by-row to set-based reasoning for analytical SQL. This mindset is prerequisite to writing efficient warehouse queries.

Lesson 2 • Aggregation and Grouping Extensions

Applies GROUPING SETS, ROLLUP, and CUBE for multi-dimensional summaries. Students produce cross-tab and subtotal reports without multiple query passes.

Lesson 3 • Common Table Expressions and Recursion

Uses CTEs to decompose complex queries and recursive CTEs for hierarchical data. Students improve query readability and support hierarchical reporting.

Lesson 4 • Query Optimization Techniques

Reads execution plans and applies indexing, partitioning, and statistics hints. Students reduce query runtimes and resource consumption in production.

Lesson 1 • Monitoring and Remediation Workflows

Builds dashboards and alert workflows that surface quality issues in near real time. Students design remediation paths that close the loop on detected defects.

Lesson 2 • Data Profiling Techniques

Applies column-level, cross-column, and cross-table profiling to discover anomalies. Profiling results feed directly into quality rule design.

Lesson 3 • Data Quality Dimensions

Defines completeness, accuracy, consistency, timeliness, and uniqueness as measurable dimensions. Students use these dimensions to scope quality assessment efforts.

Lesson 4 • Designing Data Quality Rules

Translates business requirements into executable validation rules and thresholds. Students implement rules as reusable SQL checks or pipeline assertions.

Lesson 1 • Workload Management and Concurrency

Configures resource pools, query queues, and priority classes for mixed workloads. Students balance interactive and batch workloads without resource contention.

Lesson 2 • Indexing Strategies for Analytics

Covers bitmap, B-tree, and clustered indexes in the context of warehouse queries. Students choose index types that accelerate common analytical access patterns.

Lesson 3 • Caching and Materialization

Uses result caching, materialized views, and aggregate tables to serve repeated queries. Students identify which queries benefit most from pre-computation.

Lesson 4 • Storage Layout and Compression

Examines row-store vs. column-store layouts and compression algorithms. Students select storage formats that minimize I/O for analytical scan patterns.

Lesson 1 • Cloud Warehouse Architecture Patterns

Compares shared-disk, shared-nothing, and serverless cloud architectures. Students select the architecture that fits their organization's scale and cost profile.

Lesson 2 • Infrastructure as Code for Warehouses

Provisions warehouse resources using declarative configuration templates. Students version-control infrastructure and automate environment creation.

Lesson 3 • Disaster Recovery and High Availability

Designs backup schedules, failover strategies, and recovery time objectives. Students produce a documented DR plan validated through tabletop exercises.

Lesson 4 • Security and Access Control

Implements role-based access, column-level security, and row-level filtering. Students enforce least-privilege access across all warehouse objects.

Lesson 1 • Testing and Validation Strategies

Executes unit, integration, and user-acceptance tests across models, pipelines, and reports. Students produce test evidence that satisfies business sign-off requirements.

Lesson 2 • Solution Architecture and Design

Produces architecture diagrams, data flow maps, and model designs for the full solution. Students justify design decisions against requirements and constraints.

Lesson 3 • Requirements Gathering and Scoping

Elicits analytical requirements from business stakeholders and translates them into technical specifications. Scoping prevents scope creep and aligns delivery expectations.

Lesson 4 • Agile Delivery for Warehouse Projects

Applies sprint-based delivery to warehouse builds, including backlog grooming and demos. Students adapt agile ceremonies to the iterative nature of data work.