Python is widely used in data engineering for its versatile and powerful libraries. It offers built-in data structures like lists, tuples, dictionaries, and sets. List comprehension provides a shorter syntax for creating new lists. Python can be integrated with cloud storage using libraries like gcsfs, adlfs, and s3fs. Unit testing and mocking play a vital role in ensuring the correctness of data engineering code. DataFrame libraries like Pandas, PySpark, and Polars facilitate data manipulation and analysis. DuckDB is an in-memory analytical database management system, while Faker generates synthetic data for testing.
Table of contents
Python for Data EngineeringBuild-in data structuresOperations on ListsDecoratorsData ClassConcurrency vs. parallelismIntegration with Cloud StorageUseful librariesUnit TestsSummary5 Impressions