Data Engineering Automation: Ansible, Apache Airflow, and Snowflake
Duration
28 hours of intensive training with live instruction delivered over five to ten days to accommodate varied scheduling needs.
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.
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.
- Manage inventories of hosts and groups, including 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.
- Monitor and troubleshoot Airflow workflows through logs, the web 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.
- 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.
- Apply production best practices including idempotent design, error handling, testing strategies, and performance optimization.
Training Materials
Students receive comprehensive courseware, including reference documents, code samples, and lab guides covering all three technologies. For online deliveries, 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
Infrastructure Automation with Ansible
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
Workflow Orchestration with Apache Airflow
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
- Schedulers and Pools
Installation and Configuration
- Python Virtual Environment
- Install Airflow with Constraints
- Standalone Mode
- Webserver and Scheduler
- Database Backend Options
Developing DAGs
- Operator API
- TaskFlow API
- Dynamic Task Mapping
- Templating with Jinja2
- Task Dependencies
Airflow Configuration
- Configuration File Options
- Executor Types (Local, Celery)
- Log Levels and Structure
- Environment Variables
Monitoring and Debugging
- Airflow Web UI
- Reviewing Task Logs
- Notifications and Alerts
- Troubleshooting Common Issues
Cloud Data Warehousing with Snowflake
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
Integration and Production Pipelines
Airflow-Snowflake Integration
- Snowflake Provider for Airflow
- 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