6 Best Python-Based ETL Tools to Learn in 2020

by September 11, 2020 0 comments

Data Science

Exploring the list of top Python-based ETL to Learn. 

Organizations, regardless of sizes and types, nowadays deal with ever-increasing amounts of data. This voluminous data is too complex to comprehend manually; requires effective solutions to process and assess it and derive valuable data-driven insights hidden within the data. ETL, which is an abbreviation of the Extract, Transform, and Load of data, gleans and processes data from various sources into one data store where it can then be later analyzed. It is a core component of data warehousing.

There are a lot of ETL tools out there including Java, JavaScript, Hadoop and GO, among others. But Python continues dominating the ETL space. Python developers have built a wide array of open-source tools for ETL that make it a go-to solution for complex and massive amounts of data. Let’s have a look at the 6 best python-based ETL tools to learn in 2020.



Developed by Spotify, Luigi is an open-source Python package designed to make the management of long-running batch processes easier. As a result, it can handle tasks that go far beyond the scope of ETL, along with handling ETL quite well, too. Luigi provides dependency management with stellar visualization, with failure recovery via checkpoints. It has command-line interface integration. This Python-based ETL tool is conceptually similar to GNU Make, but isn’t only for Hadoop, though, it does make Hadoop jobs easier. Luigi is currently used by a majority of companies including Stripe and Red Hat.



Pandas is one of the most popular Python libraries, providing data structures and analysis tools for Python. It adds R-Style data frames that make data manipulation, cleaning and analysis much easier than it would be in raw Python. Pandas can handle every step of the process, allowing users to derive data from most storage formats and manipulate their in-memory data quickly and easily. Once they are done, pandas makes it just as easy to write a data frame to CSV, Microsoft Excel, or a SQL database.



Bubbles is another Python framework that allows you to run ETL. It is written in Python, but designed to be technology agnostic. Bubble is set up to work with data objects, representations of the data sets being ETL’d, in order to maximize flexibility in the user’s ETL pipeline. It uses metadata to describe pipelines as opposed to script-based. ThisPython-based ETL tool has not seen active development since 2015, as a result, some of its features may be out of date.



Bonobo is a lightweight, code-as-configuration ETL framework for Python. It is incredibly easy-to-use and allows you to rapidly deploy pipelines and execute them in parallel. By learning Bonobo, anyone can excerpt from a variety of sources, e.g., CSV, JSON, XML, XLS, SQL, etc., and the entire transformation follows atomic UNIX principles. For this ETL tool, new users don’t have to learn a new API; they just familiar with Python.



petl, a Python package for ETL, which lets users build tables in Python and extract data from multiple sources such as CSV, Xls, HTML, txt, json, etc. This ETL tool has a lot of the same capabilities as pandas, but is designed more specifically for ETL work and doesn’t involve built-in analysis features, so it is best suited for users who are interested purely in ETL.


Apache Airflow

An open-source Python-based workflow automation tool, Apache Airflow used for setting up and maintaining data pipelines. Apache Airflow has a significant role to play in today’s digital age where users need to have a powerful and flexible tool that will handle the scheduling and monitoring of their jobs. Apache Airflow makes a great addition to users’ existing ETL toolbox since it’s incredibly useful for management and organization. Its open-source nature makes it easier to set up and maintain data pipelines.

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