Scaling and Optimizing Data Pipelines with Polars Course

Scaling and Optimizing Data Pipelines with Polars

IntermediateSkill Level

Updated 06/2026

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

Course Description

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.

Prerequisites

Data Transformation with Polars

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

50 XP

Previewing the data lazily

100 XP

Summarizing digital checkouts

100 XP

Collecting two reports together

100 XP

Optimization and query plans

50 XP

Reading the naive plan

100 XP

Reading the optimized plan

100 XP

What query optimization actually does

50 XP

Using optimization to full effect

50 XP

Lazy pivot to wide format

100 XP

Profiling a lazy query

100 XP

Fast-path filter on sorted data

100 XP

Start Chapter

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

50 XP

Inspecting a Parquet schema

100 XP

Summarizing Parquet data

100 XP

Writing a Parquet snapshot

100 XP

Parsing CSVs

50 XP

Parsing a messy CSV

100 XP

Overriding the inferred schema

100 XP

Ignoring parse errors

100 XP

Working with Multifile Datasets

50 XP

Scanning multiple files

100 XP

Inserting missing columns

100 XP

Scanning a hive-partitioned dataset

100 XP

Working with Databases

50 XP

Reading from SQLite with a URI

100 XP

Appending to a SQLite history table

100 XP

Querying DuckDB into Polars

100 XP

Start Chapter

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

50 XP

Extracting a primary genre

100 XP

Counting genres per movie

100 XP

Counting genre popularity

100 XP

Working with nested columns

50 XP

Unnesting production_country

100 XP

Renaming country fields

100 XP

Creating an Object dtype

100 XP

Using Categorical and Enum dtypes

50 XP

Creating a Categorical column

100 XP

Using a categorical expression

100 XP

Creating an Enum column

100 XP

Reducing memory pressure

50 XP

Inspecting numeric range

100 XP

Downcasting numeric columns

100 XP

Estimating DataFrame size

100 XP

Start Chapter

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

50 XP

Targeting the streaming engine

100 XP

Processing query results in batches

100 XP

Sinking large queries to disk

50 XP

Sinking a cleaned extract to Parquet

100 XP

Multiplexing Parquet sinks

100 XP

Using a custom batch sink

100 XP

Testing Polars Pipelines

50 XP

Comparing DataFrames with equals

100 XP

Testing schemas

100 XP

Testing full DataFrames

100 XP

Congratulations!

50 XP

Start Chapter

Scaling and Optimizing Data Pipelines with Polars

Course
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FAQs

Do I need to know Polars already before taking this course?

Yes. You should be comfortable with lazy mode, expressions, and basic operations like filtering and grouping. The Data Transformation with Polars course covers everything you need to get started.

What will I be able to build after this course?

You will be able to build fast, memory-efficient data pipelines that read from Parquet, CSV, and databases, scale to larger-than-memory datasets with streaming, and catch bugs early with built-in testing tools.

Will I learn how to connect Polars to databases?

Yes. The course covers reading from SQLite with ADBC, writing results back to database tables, and querying DuckDB with its native Polars integration.

Why should I learn about Polars dtypes like Categorical and Enum?

Choosing the right dtype can cut memory usage significantly and catch bad data before it reaches your reports. The course shows you when each dtype pays off and how to measure the difference.

Scaling and Optimizing Data Pipelines with Polars

Training a Team?

Course Description

Prerequisites

Query Optimization Deep Dive

Efficient Data Input and Output

Advanced Dtypes for Optimal Analysis

Working with Polars at Scale

Earn Statement of Accomplishment

FAQs

Do I need to know Polars already before taking this course?

What will I be able to build after this course?

Will I learn how to connect Polars to databases?

Why should I learn about Polars dtypes like Categorical and Enum?

Join over 19 million learners and start Scaling and Optimizing Data Pipelines with Polars today!

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Course Description

Earn Statement of Accomplishment

FAQs

What will I be able to build after this course?

Will I learn how to connect Polars to databases?

Why should I learn about Polars dtypes like Categorical and Enum?

Join over .css-nklxlk{color:var(--wf-brand--main, #03EF62);}19 million learners and start Scaling and Optimizing Data Pipelines with Polars today!

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Grow your data skills with DataCamp for Mobile

Join over 19 million learners and start Scaling and Optimizing Data Pipelines with Polars today!