Welcome to Containerization & Deployment! Remember how we built automated workflows? Now we'll learn to package those workflows so they run consistently anywhere.
Ever had code that "works on my machine" but fails elsewhere? Containers solve this problem.
Engagement Message
Describe one deployment challenge you've hit when moving a data script from your laptop to production?
Containers are like shipping containers for your code. They package your application with everything it needs—libraries, dependencies, configuration—into a single, portable unit.
This means your data pipeline runs identically on your laptop, staging server, and production cloud.
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Name one risk that inconsistent environments pose to a data pipeline?
Docker is the most popular containerization platform. It lets you define your application's environment in a simple text file called a Dockerfile.
Think of it as a recipe that builds identical environments every time.
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Give one benefit of defining your data environment in a Dockerfile?
Here's why containers are game-changing for data pipelines: no more "dependency hell" where different jobs conflict. Each container runs in isolation with its own libraries.
Your Python 3.8 job and Python 3.11 job can run on the same server without issues.
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How does container isolation simplify running different versions of data tools side-by-side?
Resource management is crucial for data containers. You can specify exactly how much CPU and memory each container gets, preventing one hungry job from starving others.
This is especially important for data processing which can be resource-intensive.
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What might happen if you don't limit resources for a large data transformation job?
