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If you’re thinking about getting into data engineering, you’ve probably heard the term ‘DevOps’ mentioned. But what is it, why is it relevant to data engineers, and how do you learn those skills? 

What Is DevOps?

At a simple level, DevOps is about building, deploying, and running pipelines and applications smoothly. It combines development and writing code (Dev) with operations, or running systems (Ops).

Once you’ve written some code, DevOps helps you:

Why DevOps Matters for Data Engineers

Imagine you’ve built a data pipeline on your laptop. It works perfectly, but that’s not actually enough. It will need to run every day, with more data, and be usable by other people. 

For data engineers, this means taking your data pipelines and making sure they:

DevOps gives you the tools to do so, helping you move from ‘This data pipeline works locally on my machine’ to ‘It works reliably in the cloud for everyone.’ 

Overall, DevOps is the difference between being able to build an isolated data project, and being able to run a full data system like a professional.

What You Need to Learn

There are some key DevOps-related skills that you need to know to work efficiently as a data engineer.  

These are all covered on weeks 5 and 6 of Northcoders’ Data Engineering, AI & Machine Learning Bootcamp, which trains beginners to become junior data engineers. 

  1. Running Code in the Cloud (Compute)

You need to be able to run your code on remote servers, not just locally on your own computer.

You’ll work with:

Why this matters:

  1. Storing Data Properly (Cloud Storage)

Data needs a safe, organised place to be stored in.

You’ll use:

Why this matters:

  1. Infrastructure as Code (IaC) with Terraform

Instead of manually setting things up, you can write code that builds your infrastructure for you.

With Terraform, you can:

Why this matters:

  1. CI/CD (Continuous Integration & Deployment)

This is about automating your workflow. Instead of manually updating your project every time you make a change, you can:

Why this matters:

  1. Orchestration (Making Everything Work Together)

Data pipelines often have multiple steps:

  1. Extract data
  2. Transform it
  3. Load it somewhere

Orchestration tools help you:

Why this matters:

Bringing It All Together

DevOps might sound like a big, complicated topic, but for data engineers, it’s really about one thing: making your data projects work in the real world. 

If you want to build systems that companies actually use, not just projects that sit on your laptop, these skills are essential.

If you’re excited about turning data into real, working systems, through the Data Engineering, AI & Machine Learning Bootcamp, you’ll understand how to:

You don’t need any tech experience to join the bootcamp, only foundations in Python. If you’re new to Python, Northcoders will give you access to free materials to learn what you need from scratch in your own time. 
If you’re already an experienced Python developer familiar with databases and looking to upskill in Cloud Engineering and DevOps specifically, Northcoders also offers a 2-week Upskilling in Cloud Engineering and DevOps Course.

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