Polars, Lazy Frames, DataFrames, Parallel Processing, Files, Database, Data Structures, Databases, Query Plan, ETL
What you’ll learn
- Polars Using Python
- Basic Data Structures
- Expressions in Polars
- ETL and Various Transformations
Requirements
- Basic Python Knowledge
Who this course is for:
- Data Engineers
- ETL Developers
- Data Architects
- ETL Architects
- Data Scientists
Description
DataFrames For The New Era
Polars is written from the ground up with performance in mind. Its multi-threaded query engine is written in Rust and designed for effective parallelism. Its vectorized and columnar processing enables cache-coherent algorithms and high performance on modern processors.
You will feel right at home with Polars if you are familiar with data wrangling. Its expressions are intuitive and empower you to write code which is readable and performant at the same time.
Polars is and always will be open source. Driven by an active community of developers, everyone is encouraged to add new features and contribute. Polars is free to use under the MIT license.
The course is about performing ETL (Extract, Transform, Load) using Polars in Python. The course convers the basics of Polars, Data Structures in Polars such as Series, DataFrames, .., Expressions such as Select Functionality, Operators , Renaming the Columns/ Fields and Handling Nulls. Working with the Transformations such as Filter, Sort, Join, Pivot, Concatenate, Melts and Windowing Functions.
Polars supports reading and writing to all common data formats. This allows you to easily integrate Polars with your existing data stack.
- Text: CSV & JSON
- Binary: Parquet, Delta Lake, AVRO & Excel
- IPC: Feather, Arrow
- Databases: MySQL, Postgres, SQL Server, Sqlite, Redshift & Oracle
- Cloud storage: S3, Azure Blob & Azure File
