Ir al contenido principal

Curso

Scaling and Optimizing Data Pipelines with Polars

IntermedioNivel de habilidad

Actualizado 6/2026

Learn to optimize, scale, and test Polars data pipelines for production-ready performance.

Comienza El Curso Gratis

PythonData Manipulation

4 h

15 vídeos

56 Ejercicios

4,800 XP

Certificado de logros

Preferido por estudiantes en miles de empresas

¿Formando un equipo?

Prueba para empresas

Descripción del curso

Take your Polars skills to production scale. Learn to read query plans and unlock the optimizer's full potential, work efficiently with Parquet, CSV, and database sources, and exploit advanced dtypes like List, Struct, Categorical, and Enum. You'll also stream large queries to disk, process data in batches, and build testable pipelines with built-in assertions. By the end, you'll be equipped to build high-performing data workflows that handle datasets of any size.

Requisitos previos

Data Transformation with Polars

1

Query Optimization Deep Dive

Learn how to keep queries lazy for maximum optimization, read and interpret query plans, and unlock fast paths with profiling and sorted data.

Effective query execution

Previewing the data lazily

Summarizing digital checkouts

Collecting two reports together

Optimization and query plans

Reading the naive plan

Reading the optimized plan

What query optimization actually does

Using optimization to full effect

Lazy pivot to wide format

Profiling a lazy query

Fast-path filter on sorted data

Iniciar Capítulo

2

Efficient Data Input and Output

Learn how to read and write Parquet files, parse messy CSVs, scan multifile and hive-partitioned datasets, and query databases from Polars.

Working with Parquet files

Inspecting a Parquet schema

Summarizing Parquet data

Writing a Parquet snapshot

Parsing CSVs

Parsing a messy CSV

Overriding the inferred schema

Ignoring parse errors

Working with Multifile Datasets

Scanning multiple files

Inserting missing columns

Scanning a hive-partitioned dataset

Working with Databases

Reading from SQLite with a URI

Appending to a SQLite history table

Querying DuckDB into Polars

Iniciar Capítulo

3

Advanced Dtypes for Optimal Analysis

This chapter covers working with List and Struct columns, encoding repeated strings as Categorical and Enum dtypes, and reducing memory use through numeric downcasting.

Handling array data

Extracting a primary genre

Counting genres per movie

Counting genre popularity

Working with nested columns

Unnesting production_country

Renaming country fields

Creating an Object dtype

Using Categorical and Enum dtypes

Creating a Categorical column

Using a categorical expression

Creating an Enum column

Reducing memory pressure

Inspecting numeric range

Downcasting numeric columns

Estimating DataFrame size

Iniciar Capítulo

4

Working with Polars at Scale

Learn how to use the streaming and GPU engines, sink large query results directly to disk with partitioning, and test pipelines with Polars' built-in assertions.

Working with the streaming and GPU engines

Targeting the streaming engine

Processing query results in batches

Sinking large queries to disk

Sinking a cleaned extract to Parquet

Multiplexing Parquet sinks

Using a custom batch sink

Testing Polars Pipelines

Comparing DataFrames with equals

Testing schemas

Testing full DataFrames

Congratulations!

Iniciar Capítulo

Scaling and Optimizing Data Pipelines with Polars

Curso
completo

Obtener certificado de logros

Añade esta certificación a tu perfil de LinkedIn o a tu currículum.
Compártelo en redes sociales y en tu evaluación de desempeño.Inscríbete Ahora

¡Únete a 19 millones de estudiantes y empieza Scaling and Optimizing Data Pipelines with Polars hoy mismo!

Desarrolla tus habilidades de datos con la aplicación móvil de DataCamp

Progresa desde cualquier dispositivo móvil con nuestros cursos y desafíos de programación diarios de 5 minutos.