Ansible vs. Python for Network Automation

Both Python and Ansible are excellent choices for network automation. Both, however, address distinct types of network automation issues. You have total power over creating unique workflows, logic, and connectors with Python. Ansible, on the other hand, employs a structured syntax and less code and is superior when you want more understandable, regularly repeated automation across various devices.

First, let's define Python in terms of network automation.

In network automation, what is Python?
Engineers use Python with libraries to connect devices, transmit commands, interpret output, gather data, and evaluate configurations to integrate with the APIs. Python writes the script and logic for network automation directly.
Python allows you to create workflows like:
Entering device credentials and gathering display command output
Routing table parsing and interface status
Comparing settings before and after
Using templates to create device-specific configurations
retrieving information from ticketing tools, CMDBs, or APIs
Constructing unique compliance and drift tests
Developing automation that is specific to your environment
Python's versatility in automation makes it a powerful tool. You can develop a comprehensive internal network automation platform over time or write a brief script for a single activity.

This freedom presents a problem as well. The logic must be defined, exceptions must be handled, credentials must be managed, outputs must be processed, and the code must be maintained.
In network automation, what is Ansible?

Ansible uses modules, inventories, and other playbook sets to automate processes across several systems. It is often used for repeating procedures and configuration changes in network automation.

It expresses the planned operations in a YAML format rather than developing a comprehensive program and its logic. It gradually becomes simpler to read, comprehend, and review as a result.

Ansible is very helpful for jobs like:

Routing, VLAN, ACL, or basic interface modifications
Performing the identical check on numerous devices
Regularly backing up setups
enforcing a predetermined desirable state
Verifying if devices adhere to the policy
Automating team operations in a standard manner

When operational consistency and repeatability are your primary objectives, Ansible performs effectively. Teams generally find it easier because even non-programmers can read the playbooks.

Control and abstraction are key distinctions between Python and Ansible.

You have complete control using Python. Ansible, on the other hand, provides managed abstraction.

Python gives you complete control over the automation's behavior. The steps, conditions, loops, retries, parsing, and output handling are all written by you.

A large portion of the structure is already available with Ansible. Playbooks, tasks, modules, variables, and inventory are all things you deal with. You pay less attention to each line of execution logic and more attention to what ought to occur.

One is not superior to the other because of this. Simply put, they suit various usage situations.

Ansible vs. Python for Network Automation: A Useful Comparison

The differences between the two—Python and Ansible—based on several criteria have been discussed below.

Area Ansible Python

Learning style: Easier for novices who are operations-focused; requires a programming perspective.

Extremely considerable flexibility within the framework structure

Coding style affects readability. Teams typically find it easier to read

Quickness for one-time custom jobs Excellent, but occasionally heavier

Repeatable actions across several devices robust and well-designed Outstanding right out of the box

Integrations and custom logic The best, albeit less natural, option

State-driven configuration tasks It is possible, but you have to construct it. Strong with playbooks and modules

Comfort with code determines team acceptance. Generally, mixed teams find it easier.

Debugging is accurate but requires a lot of code; it's easier for scripted workflows.

Scaling over time Outstanding with excellent engineering Outstanding for standardizing operations
When is Python a better option?

When jobs are more complex and non-repetitive and cannot be solved by predetermined models, this is a superior choice.

Python should only be used when:

1. Custom logic is needed.

Python is only the ideal option if your work requires extremely complicated decision-making, multi-condition logic, or unique rules for validation.

2. Deep parsing is required.

when complicated or confused CLI outputs are returned by network devices. Python is a superior option in certain situations.

3. Several systems must be integrated.

Assume that spreadsheets, different monitoring tools, repository systems, and direct APIs are the sources of your workflow. Python is a popular option that gives you flexibility in these situations.

4. You desire complete control and debugging

Python allows you to examine each stage. This is important when creating reliable workflows or addressing edge cases.

A script that verifies BGP peers, compares outputs with the prior state, opens a ticket if a threshold is exceeded, and transmits a condensed report to a dashboard would be an excellent example. That is an easy task.
When is Ansible a better option?

When a task is policy-driven, repeatable, and applied consistently across numerous devices, Ansible is the preferable choice.

Select Ansible when:

1. You want operational automation to happen more quickly.

Ansible typically requires less programming work if the objective is to swiftly automate common network modifications.

2. You require automation that is collaborative.

A custom Python script with numerous libraries and utility functions is frequently more difficult for multiple developers to review than a YAML playbook.

3. You desire uniformity

Common procedures like as interface configuration, VLAN rollout, ACL changes, backups, and configuration checks are ideally suited for Ansible.

4. You desire automation based on inventory

When inventory, variables, and task structures are integrated into the tool, it becomes easy to run the same workflow across groups of devices.

5. You wish to minimize code upkeep.

If you build low-level logic for playbooks instead of Python-based automation. For instance, VLAN, SNMP, DNS, and NTP vary throughout many branch switches and routers.

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