A data analyst develops a Python script that performs a monthly sentiment analysis on customer feedback. The script relies on specific versions of the pandas, NLTK, and scikit-learn libraries. When a colleague attempts to run the same script on their machine, they encounter numerous errors due to library version conflicts.
Which of the following infrastructure concepts would BEST help the analyst ensure the script and its dependencies are bundled together for consistent execution across different environments?
The correct answer is containerization. Containerization is a technology that packages an application with all of its dependencies, such as libraries and configuration files, into a single, isolated unit called a container. This ensures that the application runs reliably and consistently when moved from one computing environment to another, such as from the analyst's laptop to a colleague's machine, which directly solves the problem of library version conflicts.
A virtual machine (VM) also provides an isolated environment but does so by virtualizing the entire hardware stack, including a full guest operating system. This makes VMs much more resource-intensive and slower to start than containers, which only virtualize the operating system and share the host kernel. For packaging a single application and its dependencies, containerization is the more lightweight and efficient solution.
A shared file system allows multiple users to access the same files but does not manage the software environment or dependencies needed to run a script. The library conflicts would still occur.
A cloud data lakehouse is a data storage architecture that combines the features of a data lake and a data warehouse. It is used for storing and analyzing large amounts of data, not for packaging application execution environments.
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CompTIA Data+ DA0-002 (V2)
Data Concepts and Environments
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