Updated June 2026

Data Engineering Automation: Ansible, Apache Airflow, and Snowflake

Class Duration

28 hours of live training delivered over 5-10 half-days or 4-5 full days.

Student Prerequisites

Practical experience with Python
Basic Linux command line skills
Familiarity with SQL fundamentals
Basic understanding of cloud computing concepts

Target Audience

This course is designed for data engineers, DevOps professionals, and Python developers who want to build automated, production-ready data pipelines. Ideal participants have foundational Python and SQL skills and are looking to expand their toolkit with infrastructure automation, workflow orchestration, and modern cloud data warehousing. Whether you're a DevOps engineer moving into data platforms, a data analyst stepping into engineering, or a developer tasked with building end-to-end pipelines, this course provides the hands-on skills to provision infrastructure with code, schedule and monitor complex workflows, and deliver data to stakeholders through Snowflake. Teams who want to go deeper on the orchestration layer can continue to Apache Airflow Programming: Developing, Configuring, and Automating Workflows.

Description

This course combines three essential technologies for modern data engineering. Ansible provides infrastructure automation and configuration management, ensuring consistent and repeatable environment setup. Apache Airflow enables workflow orchestration, scheduling, and monitoring of complex data pipelines. Snowflake serves as the cloud-native data warehouse destination, offering scalability and performance for analytics workloads. Participants will learn each technology individually before integrating them into cohesive, production-ready data engineering solutions.

Learning Outcomes

Provision and configure data engineering infrastructure using Ansible playbooks, roles, and templates, managing inventories of hosts and groups with dynamic inventory for cloud environments.
Secure sensitive credentials using Ansible Vault for database connections and API keys.
Install and configure Apache Airflow with appropriate executors and database backends.
Design and implement DAGs using the Operator API, TaskFlow API, and dynamic task mapping, and monitor and troubleshoot workflows through logs, the Airflow UI, and alerting mechanisms.
Navigate Snowflake's architecture and apply best practices for virtual warehouse sizing and cost management.
Load data into Snowflake using stages, the COPY command, and Snowpipe for continuous ingestion, then query and transform semi-structured data using Snowflake SQL and window functions.
Implement role-based access control to secure Snowflake data assets.
Build end-to-end ELT pipelines that extract from source systems, orchestrate with Airflow, and load into Snowflake, applying production best practices including idempotent design, error handling, testing strategies, and performance optimization.

Training Materials

Comprehensive courseware is distributed online at the start of class. All students receive a downloadable MP4 recording of the training.

Software Requirements

Students will need a free, personal GitHub account to access the courseware. Students will need permission to install Python and Visual Studio Code on their computers, along with Python packages and VS Code extensions. A Snowflake trial account will be used for hands-on exercises. If students are unable to configure a local environment, a cloud-based environment can be provided.

Training Topics

Introduction to Ansible

Overview and Architecture
Benefits of Automation
Declarative, Push-based Configuration
YAML Syntax Review
Command Line Tools

Ansible Setup and Configuration

Installing Ansible
Configuring Ansible
Understanding Configuration Files

Inventory Management

Defining Hosts and Groups
Working with Inventory Files
Dynamic Inventory

Ansible Playbooks

Basics of Playbook Writing
Variables and Facts
Tasks and Handlers
Using Modules in Tasks
Commonly Used Modules

Working with Variables and Templates

Defining and Using Variables
Gathering Facts about Hosts
Jinja2 Templates in Ansible
Template Modules and Variables

Ansible Roles and Best Practices

Creating and Using Roles
Directory Structure
Ansible Galaxy
Writing Reusable Code

What is Apache Airflow?

Distributed Task Automation
Compared to Cron Jobs and Celery
Scalability and Reliability
Directed Acyclic Graphs (DAGs)
Workflows as Code

Airflow Architecture

Anatomy of a DAG
Operators and Tasks
Variables and XComs
Providers and Connections
Assets and Asset-Based Scheduling
Schedulers and Pools

Installation and Configuration

Python Virtual Environment
Install Airflow with Constraints
Standalone Mode
API Server, Scheduler, and DAG Processor
Database Backend Options

Developing DAGs

The airflow.sdk Authoring Interface
Operator API
TaskFlow API (@dag and @task)
Dynamic Task Mapping
Templating with Jinja2
Task Dependencies

Airflow Configuration

Configuration File Options
Executor Types (Local, Celery, Kubernetes, Edge)
Log Levels and Structure
Environment Variables

Monitoring and Debugging

The Airflow UI
Reviewing Task Logs
Notifications and Alerts
Troubleshooting Common Issues

Introduction to Snowflake

Cloud Data Warehouse Architecture
Separation of Storage and Compute
Virtual Warehouses
Snowflake Editions and Pricing
Web Interface Overview

Snowflake Objects and Data Modeling

Databases, Schemas, and Tables
Views and Materialized Views
Data Types and Constraints
Clustering Keys
Time Travel and Fail-safe

Loading Data into Snowflake

Stages (Internal and External)
COPY INTO Command
File Formats (CSV, JSON, Parquet)
Snowpipe for Continuous Loading
Data Transformation on Load

Snowflake SQL and Querying

Snowflake SQL Extensions
Semi-structured Data (VARIANT)
Window Functions
Query Optimization
Query History and Profiling

Security and Access Control

Role-Based Access Control (RBAC)
Users, Roles, and Privileges
Data Sharing
Row-Level Security
Network Policies

Snowflake Best Practices

Warehouse Sizing and Scaling
Cost Management
Performance Tuning
Data Lifecycle Management

Airflow-Snowflake Integration

Snowflake Provider for Airflow
SQLExecuteQueryOperator (replacing SnowflakeOperator)
SnowflakeSqlApiOperator
Connection Configuration
Building ELT DAGs

End-to-End Pipeline Development

Extract from Source Systems
Transform with Snowflake SQL
Orchestrate with Airflow
Error Handling and Retries
Idempotent Pipeline Design

Using Ansible for Data Infrastructure

Provisioning Airflow Environments
Managing Snowflake Users and Roles
Configuration Management
Secrets Management with Ansible Vault

Advanced Topics

Dynamic DAGs and Task Mapping
Custom Operators and Hooks
Snowflake Tasks and Streams
Change Data Capture Patterns
Data Quality Checks

Production Considerations

CI/CD for Data Pipelines
Testing Strategies
Monitoring and Alerting
Documentation Standards
Performance Optimization