popular Python packages for ETL are:
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Apache Spark: Apache Spark is a fast and general-purpose cluster computing system that includes support for ETL tasks through its Spark SQL and DataFrame APIs. It's particularly useful for handling large-scale data processing and is often used in big data environments.
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Apache Airflow: Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. It allows you to define complex ETL pipelines as directed acyclic graphs (DAGs) and execute them on a schedule or in response to events.
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Pandas-ETL: Pandas-ETL is a Python library built on top of pandas that provides additional functionality specifically for ETL tasks, such as reading and writing data from various sources, performing data transformations, and handling errors and exceptions.
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Dask: Dask is a parallel computing library that scales Python workflows. It's often used for parallelizing and distributing ETL tasks across multiple cores or machines, allowing for efficient processing of large datasets.
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Petl: Petl is a Python library for extracting, transforming, and loading tabular data. It provides a simple and expressive API for performing common ETL operations on CSV, Excel, and other tabular data formats.
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PySpark: PySpark is the Python API for Apache Spark, allowing you to write Spark applications using Python. It provides similar functionality to the Scala API, including support for ETL tasks through Spark SQL and DataFrame APIs.
