Python para Data Engineers

Python es un lenguaje bastante extenso, pero para un rol de Data Engineer, los conocimientos que se requieren saber normalmente están acotados a unos cuantos. Lo ideal es mantener el foco y concentrarse en las funciones y conocimientos específicos mínimos para un Data Engineer y crecer esta habilidad conforme se vaya requiriendo. De esta manera, podemos reducir el tiempo de aprendizaje del lenguaje.

Primero, separo la importancia de familiarizarse con Python y con su estructura y sintaxis en primera instancia, y después entrar a los conocimientos particulares necesarios para un Data Engineer. Además, dejo fuera de este artículo temas como Pandas o PySpark, a los que les dedicaré artículos por separado por su importancia.

Conceptos basicos

• Syntax and Data Structures:

  Understanding Python syntax, variables, basic data types (integers, floats, strings, booleans), and fundamental data structures like lists, tuples, dictionaries, and sets.

• Control Flow:

  Proficiency in using conditional statements (if/elif/else), loops (for, while), and error handling with try-except blocks.

• Functions:

  Defining and calling functions, understanding arguments, return values, and scope.

• File Handling:

  Reading from and writing to various file formats, including CSV, JSON, and potentially more advanced formats like Parquet or Avro.

• Object-Oriented Programming (OOP) Basics:

  Grasping concepts like classes, objects, inheritance, and encapsulation for building modular and reusable code.

Conceptos y Librerias especificas de Data Engineering

• Pandas:

  Essential for data manipulation and analysis, including reading and writing data, filtering, sorting, merging, and aggregating dataframes.

• Numpy:

  Important for numerical operations and working with arrays, especially when dealing with large datasets or mathematical computations.

• Database Connectivity:

  Utilizing libraries like SQLAlchemy, psycopg2 (for PostgreSQL), or pymysql (for MySQL) to connect to and interact with databases.

• API Interaction:

  Using the requests library to interact with RESTful APIs for data extraction or integration.

• Datetime Handling:

  Working with datetime objects, managing timezones, and converting between different date and time formats.

• Automation and Orchestration:

  Understanding how Python can be used to automate tasks, potentially including basic scripting for cron jobs or interacting with orchestration tools like Apache Airflow.

• Testing and Debugging:

  Writing unit tests for code and effectively debugging Python scripts to identify and resolve issues.

Cursos y temas en Youtube interesantes

• Learn Python in Less than 10 Minutes for Beginners (Fast & Easy)

• https://www.youtube.com/watch?v=fWjsdhR3z3c&pp=ygUNUHl0aG9uIGJhc2ljcw%3D%3D

• Python for Data Analysis: Getting Started

• https://www.youtube.com/watch?v=2_6O39UdFi0&list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

• Python Full Course for Beginners [2025]

• https://www.youtube.com/watch?v=K5KVEU3aaeQ

Cursos

Gratuitos:

• Coursera.org - Python and Pandas for Data Engineering

Con costo:

• Udemy.com - Python For Data Engineering with 500+ Coding Questions

Extras

Realizar los ejercicios y challenges de Python en paginas como HackerRank para agilizar el proceso de aprendizaje:

• HackerRank.com - Python

I’ll keep updating the article, sorry for the spanish-english mix.