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Scaling and Optimizing Data Pipelines with Polars
4.9(77)348 studentsINTERMEDIATEEnglish
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Course Overview
The Scaling and Optimizing Data Pipelines with Polars course focuses on building high-performance data workflows in Python using Polars. You will learn to interpret query execution plans, optimize Parquet and database I/O, utilize advanced data types like List and Struct, stream large data queries to disk, and enforce pipeline reliability with assertions.
Who Is This Course For?
Targeted at intermediate data engineers and Python data analysts aiming to accelerate data processing workflows and manage large-scale data using Polars.
Key Takeaways
- Read and interpret Polars query plans to unlock query optimization strategies.
- Efficiently ingest and query data from Parquet, CSV, and relational database sources.
- Leverage complex Polars data types including List, Struct, Categorical, and Enum.
- Build streaming batch pipelines to disk and implement testable assertion checks.
About this course
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.
Price
Free
This course is free to enrol.
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