10 Popular Python-Based ETL Tools To Learn

ETL Tools

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ETL stands for Extract Transform Load, which is a crucial procedure in the process of data preparation. With the help of ETL, one can easily access data from various interfaces. This means it can collect and migrate data from various data structures across various platforms.

In this article, we list down 10 Python-Based top ETL tools.

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(The list is in alphabetical order)

1| Apache Airflow

Apache Airflow is a Python-based workflow automation tool, which can be used to author workflows as Directed Acyclic Graphs (DAGs) of tasks. It uses all Python features to create your workflows, including date-time formats for scheduling tasks and loops to dynamically generate tasks. This platform can be used for building machine learning models, transferring data or managing data infrastructure.

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2| Bonobo

Bonobo is a popular Python-based lightweight Extract-Transform-Load (ETL) framework. This framework provides tools for building data transformation pipelines, using plain Python primitives, and executing them in parallel. Bonobo uses plugins to display the status of an ETL job during and after it runs.

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3| Bubbles

Bubbles is a Python framework for data processing and data quality measurement. The basic concept of this tool includes abstract data objects, operations and dynamic operation dispatch. The goal of this framework includes understandability of the process, auditability of the data being processed, among others. 

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4| Etlalchemy

Etlalchemy is an open-sourced Python-based application, which sits on top of SQLAlchemy and allows ETL (Extract, Transform, Load) functionality between any 2 SQL databases. The tool presents a “Simple over Complex” solution to the problem, allowing you to migrate any SQL database with four lines of code.

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5| Etlpy

Etlpy is a Python-based library to extract fields from sources (xml, csv, json, rss, etc.), transform (etlpy.resolve) from an internal representation to ORM models and load fields to a database. The features of this library include independent from external sources and from DB models, has the configuration to pick up connections between source fields and model fields, has unit-tests and more. 

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6| Luigi

Luigi is a Python-based package, which helps a user to build complex pipelines of batch jobs. The purpose of this tool is to address all the plumbing typically associated with long-running batch processes such as Hadoop jobs, dumping data to/from databases, running machine learning algorithms, or anything else.

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7| mETL

Mito ETL or mETL is a Python-based ETL tool, which has been especially designed to load elective data necessary for CEU. This tool is designed to load practically any kind of data and supports processing with the most widespread transforms, program structures and mutation steps.

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8| pygrametl

pygrametl is an open-source Python framework, which offers commonly used functionality for development of Extract-Transform-Load (ETL) processes. The tool works with both CPython and Jython so that a user can also use existing Java code and JDBC drivers in the ETL program.

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9| petl

petl is a general-purpose Python package for extracting, transforming and loading (ETL) tables of data. The design goals behind this tool include easy usability, focus on transformation as well as support exploratory analysis.

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10| Pandas

Pandas is a fast, powerful, flexible and easy to use open-source data analysis and manipulation tool. It includes an efficient DataFrame object, which is used for analysing datasets. It has intelligent data alignment as well as integrated handling of missing data, which serves as a perfect ETL tool to gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form.

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Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

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