How to Use Bash & Python for Real DevOps Automation – Full Handbook with 5 Production Use Cases

                     How to Use Bash & Python for Real DevOps Automation – Full Handbook with 5 Production Use Cases
               



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                     How to Use Bash & Python for Real DevOps Automation – Full Handbook with 5 Production Use Cases
               
Categoria: Tutoriais | FreeCodeCamp Premium
Idioma Principal: Português (Conteúdo de Tecnologia)

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Automation scripts often validate process completion instead of system health.

A Kubernetes pod can be running while the application inside it can't authenticate to the database. A Terraform deployment can return clean while someone has manually changed infrastructure in the cloud console. A canary rollout can show zero errors while users wait five seconds for every request.

The problem isn't the tooling. The problem is that the system can look healthy when it really is not.

This handbook walks through five production-style automation scenarios using Bash and Python for:

• Detecting abnormal AWS spend before the monthly invoice arrives

• Correlating logs across multiple services using trace IDs

• Finding infrastructure drift outside Terraform

• Validating secret rotation at the application level

• Automatically rolling back slow deployments before users complain

By the end of this handbook, you'll be able to build small scripts that help you notice when something is wrong in a system, even when the tools say everything is fine.

The scripts are intentionally small. The important part is the operational thinking behind them like what signal the script measures, what failure mode it can detect, and what assumptions the platform is making underneath.

Each use case includes a runnable demo environment, the complete script, a breakdown of the system behaviour involved, and an intentional failure you can trigger yourself.

If you're new to this workflow, start with use case 1 and work forward. The later sections build on the same pattern: automation is useful when it verifies reality, not just process completion.

Prerequisites

Before you start, set up the following:

• Python 3.8 or higher – check with

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python3 --version
• A Python virtual environment – create one before installing anything:

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python3 -m venv venv
source venv/bin/activate

# on Windows:

venv\Scripts\activate

This keeps your installed packages isolated from your system Python and prevents permission errors on shared machines.

• pip – Python's package installer, included with Python

• AWS CLI configured with a working profile – a free-tier AWS account is enough for use cases 1, 3, and 4. Verify it's working with:

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aws sts get-caller-identity

• Docker and Docker Compose – needed for use cases 2, 4, and 5

• Kind (Kubernetes in Docker) – a way to run Kubernetes locally for use cases 4 and 5. Install with

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brew install kindon macOS, or follow the Kind quick start guide

• kubectl – the command-line tool for talking to a Kubernetes cluster. After installing Kind, run

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kind create clusterand kubectl is configured automatically

• Helm – a package manager for Kubernetes, needed for use case 5. Install with

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brew install helmor the Helm install guide

• Terraform – needed for use case 3. Install with

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brew install terraformon macOS or follow the Terraform install guide. Check with

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terraform version.

• bc – a calculator utility used by the canary watch scripts for floating-point comparison. Install with

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brew install bcon macOS or

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apt install bcon Ubuntu. Run

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bc --versionto confirm it is available before starting use case 5.

Knowledge and Skills

• You should be comfortable reading Python and Bash scripts without needing to write them from scratch.

• You should have basic Linux terminal com

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