Top 5 Python ETL Tools for Every Data Scientist

IndustryTrends

Apache Airflow: A platform to programmatically author, schedule, and monitor workflows, known for its scalability and ease of use.

Pandas: While primarily a data manipulation library, Pandas is often used for ETL tasks due to its powerful data processing capabilities.

Apache Spark: Provides efficient data processing with its in-memory computing capabilities, suitable for large-scale ETL operations.

Luigi: A Python module that helps you build complex pipelines of batch jobs, handling dependency resolution, workflow management, and task scheduling.

Bonobo: A lightweight framework for building ETL processes, focusing on simplicity and extensibility, suitable for small to medium-sized projects.

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