Infrastructure as Code 2.0: From Puppet Classes to Ansible Galaxy Collections - Complete 2025 Guide

Infrastructure as Code 2.0: The Evolution from Puppet Classes to Ansible Galaxy Collections

Infrastructure as Code 2.0: The Evolution from Puppet Classes to Ansible Galaxy Collections

The infrastructure automation landscape has undergone a remarkable transformation over the past two decades. What started as a revolutionary shift from manual server management to code-driven infrastructure has evolved into an ecosystem of powerful, accessible tools that democratize DevOps practices. If you're working in DevOps, SRE, or infrastructure engineering, you've likely witnessed this transition firsthand—from meticulously crafted Puppet manifests to the streamlined elegance of Ansible Galaxy collections. Understanding this evolution isn't just about knowing tools; it's about mastering the philosophy that drives modern infrastructure management.

The Puppet Era: When Configuration Management Became Code

When Puppet emerged in 2005, it fundamentally changed how operations teams approached infrastructure. Before Puppet, system administrators manually configured servers, documented procedures in wikis, and hoped their notes stayed current. Puppet introduced a declarative approach that let teams define desired system states using code, creating reproducibility and consistency that manual processes could never achieve.

Puppet's architecture centered around a master-agent model that made sense for its time. A central Puppet master compiled catalogs—complete descriptions of how systems should be configured—and distributed them to Puppet agents running on managed nodes. These agents would then enforce the desired state, applying changes as needed and reporting results back to the master. This approach provided centralized control, detailed reporting, and automatic drift correction that kept systems compliant even when administrators made manual changes.

The Puppet DSL (Domain Specific Language) became the vocabulary of infrastructure automation. Classes organized resources into logical units, modules packaged related classes together, and the Puppet Forge became a repository where the community shared solutions. Organizations could install a module for Apache, MySQL, or virtually any technology, customize it to their needs, and deploy configurations across their fleet with confidence.

However, as infrastructure evolved, Puppet's limitations became increasingly apparent. The learning curve proved steep for new team members who needed to understand Puppet's unique syntax, catalog compilation process, and resource ordering rules. The master-agent architecture added operational overhead—teams needed to maintain the Puppet infrastructure itself, managing certificates, securing communications, and ensuring the master server remained available. For organizations moving toward cloud-native architectures and continuous delivery, these complexities began to feel like friction rather than features.

Why the Industry Pivoted to Ansible

Ansible arrived in 2012 with a radically different philosophy: simplicity should not be sacrificed for capability. Its agentless architecture immediately eliminated one of the biggest pain points in configuration management. Instead of installing and maintaining agents on every managed node, Ansible simply used SSH—the ubiquitous protocol already present in every Unix-like environment. No additional infrastructure to manage, no certificates to rotate, no agents to troubleshoot.

The decision to use YAML for playbooks was transformative. YAML's human-readable structure meant that even team members new to automation could understand what a playbook did just by reading it. Compare a Puppet manifest's DSL syntax with an Ansible playbook's straightforward structure, and the difference in accessibility becomes immediately clear. This approachability didn't mean Ansible was less powerful; it meant more people could harness that power effectively.

Ansible's adoption accelerated significantly after Red Hat acquired it in 2015, bringing enterprise backing and integration into Red Hat's broader automation strategy. Organizations appreciated Ansible's imperative approach where playbooks described sequential steps to achieve a goal. While purists debated whether imperative or declarative was "better," practitioners found Ansible's model intuitive—it matched how they already thought about configuration tasks.

The flexibility Ansible offered proved crucial for modern DevOps workflows. Need to run an ad-hoc command across your fleet? Ansible makes it trivial. Want to integrate automation into CI/CD pipelines? Ansible's straightforward execution model fits naturally. Require idempotent operations that safely run multiple times? Ansible modules handle that by default. This versatility, combined with low barriers to entry, drove rapid adoption across organizations of all sizes.

Ansible Galaxy: Revolutionizing Code Reusability

Ansible Galaxy emerged as the community hub for Ansible content, functioning as a marketplace for roles—reusable automation units that encapsulate related tasks. Installing a role from Galaxy is as simple as running ansible-galaxy role install, and incorporating it into your playbooks requires just a few lines of YAML. This ease of sharing accelerated knowledge transfer across the community, letting organizations benefit from solutions that others had already perfected.

The introduction of collections in Ansible 2.9 represented a major leap forward in content distribution. While roles organize tasks, collections package complete ecosystems. Collections are a distribution format for Ansible content that can include playbooks, roles, modules, and plugins. This packaging format solved critical problems in the Ansible ecosystem.

Collections enabled technology vendors to distribute official, supported integrations for their platforms. The cisco.ios collection provides comprehensive modules for managing Cisco IOS devices. The amazon.aws collection offers extensive AWS integration. These official collections receive updates independently of Ansible core releases, meaning you can get new features and bug fixes without waiting for the next Ansible version.

The namespace system in collections eliminated naming conflicts that plagued earlier Ansible versions. With collections organized under namespaces like community.general or ansible.posix, multiple authors can create content without worrying about clashing names. Explicit versioning allows you to pin specific collection versions in your requirements, ensuring reproducibility across environments—a critical requirement for production infrastructure.

Modern collections have transformed how teams build automation. Instead of writing custom modules for common tasks, you can often find a well-maintained collection that already handles your use case. Need to manage Kubernetes resources? The kubernetes.core collection has you covered. Working with Windows systems? The ansible.windows collection provides native Windows module support. This ecosystem approach accelerates development while maintaining quality through community review and vendor support.

Technical Comparison: Beyond the Surface

When evaluating Puppet versus Ansible, the differences extend far deeper than agent-based versus agentless architecture. Puppet's continuous enforcement model checks and corrects system state regularly, typically every 30 minutes. This approach provides automatic drift remediation—if someone manually changes a configuration, Puppet will revert it on the next run. For highly regulated environments where compliance is paramount, this constant enforcement provides valuable assurance.

Ansible's on-demand execution model offers different advantages. Playbooks run when you invoke them, giving you precise control over when changes occur. This predictability aligns well with change management processes and continuous delivery pipelines where you want explicit control over deployment timing. Ansible is generally better than Puppet for most modern DevOps workflows due to its simpler setup, agentless architecture, and use of YAML, which makes playbooks easier to write and maintain.

The language difference profoundly impacts team adoption and maintenance. Puppet's DSL requires learning Ruby-adjacent syntax and understanding concepts like resource relationships, dependency ordering, and catalog compilation. While powerful, this specialized knowledge creates a barrier to entry. Ansible's YAML playbooks use standard data serialization syntax that many engineers already know from other tools. This familiarity reduces training time and makes automation more accessible to diverse team members.

Secret management illustrates how the tools approach common challenges differently. Puppet traditionally relied on Hiera for hierarchical data storage, requiring separate encryption tools like eyaml or integration with external secret managers. Ansible built secret management directly into the core product with Ansible Vault, allowing you to encrypt variables or entire files using commands native to Ansible. While both tools now integrate well with modern secret management platforms like HashiCorp Vault, Ansible's native solution remains simpler for basic use cases.

Performance characteristics differ based on architecture. Ansible leads the configuration management market with a 31.7% share, while Puppet holds around 14.6%, reflecting Ansible's broader adoption. Puppet's agent-based approach distributes the computational load of enforcing configurations across managed nodes, making it efficient at scale. Ansible's controller-based model concentrates execution on the control node, which can become a bottleneck for extremely large deployments, though parallelization and strategic use of delegation can mitigate this.

Migration Strategies: From Legacy to Modern

Migrating from Puppet to Ansible requires more than translating code—it demands rethinking your approach to infrastructure automation. The most successful migrations begin with careful inventory and prioritization. Identify components with minimal complexity and dependencies that you can migrate quickly to build team confidence and demonstrate value.

A hybrid approach works exceptionally well during transition periods. Continue using Puppet for legacy systems while implementing new services with Ansible. This coexistence lets your team learn Ansible gradually without risking critical infrastructure. Use clear inventory segmentation to ensure each tool manages distinct node groups, preventing conflicts between the two systems.

Leverage Ansible Galaxy collections to accelerate your migration. Instead of recreating functionality from scratch, search for collections that replicate what your Puppet modules accomplished. Most major technologies have well-maintained official or community collections. The ansible.posix collection provides essential Unix/Linux modules, while community.general offers hundreds of modules for diverse use cases. Reviewing these collections can also teach you Ansible best practices by examining how experienced contributors structure their automation.

Documentation becomes critical during migration. Don't just translate Puppet code to Ansible—capture the reasoning behind your Puppet designs and adapt those insights to Ansible patterns. Document decisions about playbook structure, variable organization, and role design. This knowledge base proves invaluable when questions arise months later or when onboarding new team members who never used Puppet.

Testing should be comprehensive and automated. Tools like Molecule provide frameworks for testing Ansible roles and playbooks in isolated environments. Implement syntax checking with ansible-lint, validate idempotency by running playbooks multiple times, and execute integration tests before deploying to production. This testing discipline catches issues early and builds confidence in your Ansible automation.

Enterprise Best Practices for Ansible Collections

Treat your Ansible automation as production code, not disposable scripts. Every playbook, role, and custom collection should live in version control with complete history and mandatory peer review. Pull requests aren't bureaucracy—they're your safeguard against configuration mistakes that could impact production. Establish a review process where team members examine changes for correctness, security implications, and adherence to organizational standards.

Directory structure profoundly impacts long-term maintainability. Adopt a consistent layout that clearly separates inventories, group variables, playbooks, and roles. Many successful organizations maintain separate repositories for inventories and executable content, allowing different update cycles and access controls. This separation also simplifies managing multiple environments—development, staging, and production inventories can live in separate repos while sharing the same playbook repository.

Private collections are essential for proprietary business logic. While public collections from Ansible Galaxy work well for general infrastructure components, your organization's specific workflows and integrations need secure, private distribution. Red Hat's Automation Hub provides enterprise-grade private collection hosting, but open-source alternatives like Pulp offer similar functionality. Private collections let you encapsulate organizational knowledge while maintaining the modularity and versioning benefits of the collection format.

Security should be embedded throughout your automation. Use Ansible Vault or external secret managers like HashiCorp Vault for sensitive data—never commit plaintext credentials to version control. Implement role-based access control (RBAC) if using Ansible Automation Platform, ensuring team members can only execute playbooks appropriate to their role. Regularly audit your playbooks for security anti-patterns like running tasks unnecessarily as root or disabling host key checking.

The Future of Infrastructure as Code

Infrastructure automation continues evolving rapidly, with tools specializing in their core strengths. Terraform dominates infrastructure provisioning, defining resources across cloud providers, while Ansible and competitors excel at configuration management and application deployment. This specialization benefits practitioners who can choose the best tool for each aspect of their infrastructure rather than forcing one tool to handle everything.

GitOps practices are transforming how teams apply infrastructure as code. Rather than manually executing playbooks, GitOps systems watch repository changes and automatically apply updates when code commits occur. Ansible Automation Platform and open-source AWX facilitate these workflows, connecting Git events to automated execution. This approach enforces the principle that all infrastructure changes flow through version control, improving auditability and reducing configuration drift.

Immutable infrastructure patterns challenge traditional configuration management assumptions. Instead of updating running servers, immutable approaches build complete machine images with Packer or container images with Docker, then deploy fresh instances. Ansible remains relevant in this model but shifts focus—building golden images rather than configuring running systems. This change reduces complexity and improves reliability by eliminating drift entirely.

Kubernetes integration represents the convergence of traditional and containerized infrastructure. The kubernetes.core collection enables declarative Kubernetes resource management through Ansible, bridging the gap between legacy infrastructure automation and cloud-native orchestration. Organizations running hybrid environments can use familiar Ansible patterns to manage both traditional servers and Kubernetes workloads, maintaining consistency across their entire infrastructure stack.

Choosing the Right Tool for Your Context

There's no universal "best" infrastructure automation tool—context determines the optimal choice. Puppet still excels in scenarios requiring continuous convergence and detailed compliance reporting. Organizations in highly regulated industries appreciate Puppet's continuous enforcement and comprehensive auditing capabilities. The master-agent architecture that adds complexity for some environments provides centralized control and resilience for others.

Ansible shines when simplicity, rapid adoption, and flexibility are priorities. Its agentless architecture eliminates operational overhead, letting small teams achieve impressive results quickly. Ansible Galaxy serves as a central repository for Ansible content, democratizing access to professional-grade automation. Teams can install community collections and immediately benefit from solutions that would take months to develop internally.

Evaluate your specific requirements honestly. Consider your team's size and existing skills. Assess your infrastructure's complexity and change frequency. Think about your compliance requirements and reporting needs. The best tool is the one your team can master and maintain effectively, delivering consistent value without becoming an operational burden itself.

The evolution from Puppet to Ansible Galaxy represents more than technological progress—it reflects DevOps maturity as a discipline. The industry has moved from prioritizing power and control toward valuing accessibility and collaboration. Modern infrastructure as code should be approachable enough that diverse teams can contribute while remaining robust enough to manage complex production environments. Whether you choose Puppet, Ansible, or another tool, the principles remain constant: infrastructure should be versioned, reproducible, testable, and collaboratively managed. Master these principles, and you'll succeed regardless of which specific tools your organization adopts.