Analytics Engineering Course

Lesson 1 • The Modern Data Stack Overview

Maps the categories of tools that compose a contemporary data platform. Provides vocabulary used throughout the course.

Lesson 2 • Version Control Basics for Data Teams

Covers Git workflows adapted for analytics codebases. Connects source control to collaborative, reproducible data work.

Lesson 3 • SQL Refresher for Analytics

Reviews SQL constructs most critical for analytical transformations. Ensures a common baseline before advanced query patterns are introduced.

Lesson 4 • Data Modeling Fundamentals

Introduces relational concepts and modeling paradigms used to structure analytical data. Grounds students in theory before hands-on transformation work.

Lesson 1 • Advanced SQL for Warehouses

Extends SQL skills to warehouse-specific functions and performance patterns. Builds query-writing fluency required for complex transformations.

Lesson 2 • Loading and Staging Data

Covers bulk loading, file formats, and staging area patterns for raw data ingestion. Connects ingestion to the transformation workflow.

Lesson 3 • Access Control and Data Governance Basics

Introduces role-based access, data masking, and schema-level permissions. Establishes governance habits before production pipelines are built.

Lesson 4 • Cloud Warehouse Architecture

Explains compute-storage separation, virtual warehouses, and query execution models. Provides the architectural context needed to write efficient SQL.

Lesson 1 • Documentation and the dbt DAG

Teaches model descriptions, column-level docs, and DAG visualization. Produces self-documenting pipelines that support team collaboration.

Lesson 2 • Incremental Models and Performance

Deep-dives into incremental strategies for large tables. Balances freshness, cost, and correctness in production pipelines.

Lesson 3 • Building Models and Materializations

Covers SQL model files, ref() function, and materialization types. Teaches how dbt compiles and executes transformation logic.

Lesson 4 • Macros, Packages, and Jinja

Extends dbt with reusable Jinja macros and community packages. Reduces code duplication and accelerates project development.

Lesson 5 • dbt Project Structure and Setup

Walks through project initialization, directory layout, and profile configuration. Establishes the project scaffold used in all subsequent dbt work.

Lesson 1 • Reusability and Modularity Patterns

Teaches DRY principles applied to data models through macros and shared staging layers. Reduces maintenance burden in growing projects.

Lesson 2 • Handling Complex Data Relationships

Addresses many-to-many relationships, bridge tables, and hierarchies. Equips students to model real-world business complexity accurately.

Lesson 3 • Dimensional Modeling in Practice

Applies star schema design to real analytical use cases. Translates theory from Chapter 1 into production-ready dbt models.

Lesson 4 • One Big Table vs. Normalized Marts

Compares wide denormalized tables with normalized mart structures. Guides students in choosing the right pattern for each use case.

Lesson 1 • Alerting, Monitoring, and Remediation

Builds alerting pipelines and incident response workflows for data quality failures. Closes the loop between detection and resolution.

Lesson 2 • Great Expectations and Custom Frameworks

Applies a dedicated data quality framework alongside dbt tests. Provides richer validation capabilities for complex quality requirements.

Lesson 3 • Statistical and Distribution Testing

Introduces volume checks, distribution monitoring, and anomaly detection for numerical data. Catches subtle data drift that structural tests miss.

Lesson 4 • Data Quality Dimensions

Defines completeness, accuracy, consistency, timeliness, and uniqueness as measurable quality dimensions. Frames the testing strategy for the entire chapter.

Lesson 1 • Building Workflows with Airflow

Covers Airflow DAG authoring, operators, and connections. Translates orchestration concepts into working pipeline code.

Lesson 2 • Monitoring and Observability for Pipelines

Introduces pipeline metadata, run history, and external monitoring integrations. Provides visibility into pipeline health at scale.

Lesson 3 • Scheduling and Dependency Management

Teaches cron expressions, data-aware scheduling, and cross-DAG dependencies. Ensures pipelines run in the correct order with correct timing.

Lesson 4 • Orchestration Concepts and Tools

Introduces DAG-based orchestration, task dependencies, and the orchestrator landscape. Provides conceptual grounding before hands-on tool work.

Lesson 1 • Cost Management in Production

Applies warehouse cost controls, query budgets, and resource tagging to production pipelines. Balances performance with operational cost.

Lesson 2 • Incident Response for Data Pipelines

Establishes runbooks, on-call practices, and postmortem processes for data incidents. Reduces mean time to recovery for production failures.

Lesson 3 • Secrets and Configuration Management

Covers secure credential storage, environment variables, and secrets managers. Prevents credential exposure in analytics codebases.

Lesson 4 • CI/CD for Data Pipelines

Implements automated testing and deployment pipelines for dbt projects. Brings software engineering discipline to data transformation workflows.

Lesson 1 • Dimension and Entity Modeling

Designs entity relationships and dimension hierarchies within the semantic layer. Enables consistent slicing and filtering across all metrics.

Lesson 2 • The Metrics Layer Concept

Explains why a centralized metrics layer eliminates metric inconsistency across tools. Motivates the semantic modeling work that follows.

Lesson 3 • Defining Metrics with dbt Semantic Layer

Covers MetricFlow syntax for defining measures, dimensions, and metrics in dbt. Produces reusable metric definitions consumable by any BI tool.

Lesson 4 • Governance and Versioning of Metrics

Establishes ownership, change management, and versioning practices for metric definitions. Maintains trust in metrics as the business evolves.