Ingeniero de Datos Profesional en Python

Descubre los componentes clave de la arquitectura de datos moderna: ingestión, servicio, gobernanza y orquestación.

La línea de comandos Unix permite combinar programas, automatizar tareas y ejecutar en clústeres y nubes.

Learn the essentials of VMs, containers, Docker, and Kubernetes. Understand the differences to get started!

"Este curso introduce dbt para modelado de datos, transformaciones, pruebas y creación de documentación."

Descubre los conceptos básicos de la programación orientada a objetos (OOP) y crea clases y objetos personalizados.

Domina NoSQL y mejora los flujos de datos. Aprende Snowflake, Postgres JSON y Redis.

In this Introduction to DevOps, you’ll master the DevOps basics and learn the key concepts, tools, and techniques to improve productivity.

Domina las pruebas en Python: Aprende métodos, crea verificaciones y asegura código sin errores con pytest y unittest.

Sometimes, things that once worked perfectly suddenly hit a snag. Practice your knowledge of DataFrames to find the problem and fix it!

Obtén una introducción a Docker y descubre su importancia en el kit de herramientas de un profesional de datos.

In this chapter, you'll learn how Spark manages data and how can you read and write tables from Python.

In this chapter, you'll learn about the pyspark.sql module, which provides optimized data queries to your Spark session.

This chapter introduces the exciting world of Big Data, as well as the various concepts and different frameworks for processing Big Data. You will understand why Apache Spark is considered the best framework for BigData.

The main abstraction Spark provides is a resilient distributed dataset (RDD), which is the fundamental and backbone data type of this engine. This chapter introduces RDDs and shows how RDDs can be created and executed using RDD Transformations and Actions.

In this chapter, you'll learn about Spark SQL which is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as a distributed SQL query engine. This chapter shows how Spark SQL allows you to use DataFrames in Python.

Step into a data engineer's shoes and master data cleaning with PySpark on an e-commerce orders dataset!

In this chapter, we learn how to download data files from web servers via the command line. In the process, we also learn about documentation manuals, option flags, and multi-file processing.

In the last chapter, we bridge the connection between command line and other data science languages and learn how they can work together. Using Python as a case study, we learn to execute Python on the command line, to install dependencies using the package manager pip, and to build an entire model pipeline using the command line.

Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.

Master Apache Kafka! From core concepts to advanced architecture, learn to create, manage, and troubleshoot Kafka for real-world data streaming challenges!

En este curso, aprenderás los fundamentos de Kubernetes y a desplegar contenedores con Manifests y kubectl.

Understand how data engineering can impact your business.