Build your ultimate AI agent
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.
Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Read query plans and let the Polars optimizer make your queries fast.
- Load data efficiently from Parquet, CSV, and databases.
- Use advanced dtypes to model richer data and save memory.
- Stream and batch large datasets that don't fit in memory.
- Build and test reliable, production-ready pipelines.
Prerequisites
Curriculum
Course outline
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 execution50 XP
- Previewing the data lazily100 XP
- Summarizing digital checkouts100 XP
- Collecting two reports together100 XP
- Optimization and query plans50 XP
- Reading the naive plan100 XP
- Reading the optimized plan100 XP
- What query optimization actually does50 XP
- Using optimization to full effect50 XP
- Lazy pivot to wide format100 XP
- Profiling a lazy query100 XP
- Fast-path filter on sorted data100 XP
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.
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.
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.
Scaling and Optimizing Data Pipelines with Polars
Course
Complete
