Saltar al contenido principal

Curso

Cleaning Data with PySpark

Avanzado

Actualizado 5/2025

Learn how to clean data with Apache Spark in Python.

Comienza el curso gratis

Incluido conPremium or Teams

SparkData Preparation4 horas16 vídeos53 Ejercicios4,150 XP29,145Certificado de logros

Crea Tu Cuenta Gratuita

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.

¿Entrenar a 2 o más personas?

Probar DataCamp for Business

Preferido por estudiantes en miles de empresas

Descripción del curso

Working with data is tricky - working with millions or even billions of rows is worse. Did you receive some data processing code written on a laptop with fairly pristine data? Chances are you’ve probably been put in charge of moving a basic data process from prototype to production. You may have worked with real world datasets, with missing fields, bizarre formatting, and orders of magnitude more data. Even if this is all new to you, this course helps you learn what’s needed to prepare data processes using Python with Apache Spark. You’ll learn terminology, methods, and some best practices to create a performant, maintainable, and understandable data processing platform.

Prerrequisitos

Intermediate Python Introduction to PySpark

1

DataFrame details

Iniciar capítulo

Intro to data cleaning with Apache Spark

Data cleaning review

Defining a schema

Immutability and lazy processing

Immutability review

Using lazy processing

Understanding Parquet

Saving a DataFrame in Parquet format

SQL and Parquet

2

Manipulating DataFrames in the real world

Iniciar capítulo

DataFrame column operations

Filtering column content with Python

Filtering Question #1

Filtering Question #2

Modifying DataFrame columns

Conditional DataFrame column operations

when() example

When / Otherwise

User defined functions

Understanding user defined functions

Using user defined functions in Spark

Partitioning and lazy processing

Adding an ID Field

IDs with different partitions

More ID tricks

3

Improving Performance

Iniciar capítulo

Caching a DataFrame

Removing a DataFrame from cache

Improve import performance

File size optimization

File import performance

Cluster configurations

Reading Spark configurations

Writing Spark configurations

Performance improvements

Normal joins

Using broadcasting on Spark joins

Comparing broadcast vs normal joins

4

Complex processing and data pipelines

Iniciar capítulo

Introduction to data pipelines

Quick pipeline

Pipeline data issue

Data handling techniques

Removing commented lines

Removing invalid rows

Splitting into columns

Further parsing

Data validation

Validate rows via join

Examining invalid rows

Final analysis and delivery

Dog parsing

Per image count

Percentage dog pixels

Congratulations and next steps

Cleaning Data with PySpark

Curso
Completo

Obtener certificado de logros

Añade esta credencial a tu perfil, currículum vitae o CV de LinkedIn
Compártelo en las redes sociales y en tu evaluación de desempeño

Incluido conPremium or Teams

Inscríbete ahora

Únete a más 16 millones de estudiantes y empezar Cleaning Data with PySpark hoy

Crea Tu Cuenta Gratuita

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.