klotz: infrastructure as code*

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  1. Plural is bringing AI into the DevOps lifecycle with a new release that leverages a unified GitOps platform as a RAG engine. This provides AI-powered troubleshooting, natural language infrastructure querying, autonomous upgrade assistance, and agentic workflows for infrastructure modification, all with enterprise-grade guardrails.
  2. AI is revolutionizing Infrastructure as Code (IaC), enhancing speed, intelligence, and responsiveness. However, human expertise remains crucial for understanding AI-generated outputs and ensuring proper system functionality.
  3. While current large language models (LLMs) can generate syntactically correct Terraform HCL code, they often miss critical elements like permissions, event triggers, and best practices. Iterative refinement with developer input is necessary to produce deployable, functional stacks. The article suggests using tools like Nitric to provide application context and enforce security, dependencies, and best practices.
  4. This article provides a cheatsheet on the Infrastructure as Code (IaC) landscape, highlighting the benefits of scalable infrastructure provisioning in terms of availability, scalability, repeatability, and cost-effectiveness. It discusses strategies such as containerization, container orchestration, and tools like Terraform, Kubernetes, and Ansible. The article also introduces GitOps as a method for automating infrastructure updates through Git workflows and CI/CD.
  5. Despite the great value Infrastructure as Code (IaC) tools have brought, DevOps teams are facing frustration due to tool fragmentation, integration hassles, and configuration nightmares. Emerging practices like Infrastructure from Code are being explored as potential solutions.
  6. The article discusses how despite the variety of languages used in Infrastructure as Code (IaC) tools, the challenges faced are inherent to the configuration generation approach rather than the language itself, making ecosystems and familiarity more influential in tool selection than language capabilities.
  7. Learn data engineering through free courses, tutorials, books, tools, guides, roadmaps, practice exercises, projects, and other resources.

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