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- Data
Data Processing with Polars
This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes: 1. Ingest and Write Columnar Data with Polars (video course) 2. Build Scalable Transformations with Lazy Polars (video course) 3. Transform and Curate a Partitioned Sales Dataset with Lazy Polars (code lab) 4. Implement Incremental Processing and Data Quality with Polars (video course) 5. Process Incremental Partitions with Quality Gates in Polars (code lab) 6. Tune Performance and Deploy Polars Jobs (video course)
Polars is a high-performance, columnar DataFrame engine designed for data engineering workloads that need fast, memory-efficient batch processing. In this path, you’ll learn how to ingest and write columnar datasets, build scalable transformations with lazy execution, and implement incremental processing patterns that handle late data and safe re-runs. You’ll also add practical quality gates, traceable metadata, and performance tuning techniques to productionize Polars jobs that generate trusted curated outputs.
Content in this path
Data Processing with Polars
Watch the following courses to learn core data engineering tasks with Polars.
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What You'll Learn
- How to ingest and write columnar data with Polars
- How to build scalable transformations with lazy Polars
- How to implement incremental processing and data quality with Polars
- How to tune performance and deploy Polars jobs
- Learners interested in this path should be comfortable writing basic Python (functions, reading/writing files, virtual environments) and understand core data engineering concepts like schemas, partitions, joins, and batch ETL. Prior experience with any DataFrame library (Pandas, Spark, etc.) is helpful but not required.
- Python
- data processing
- data transformation
- data engineering
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