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Python Used for ETL


Extract, Transform, and Load (ETL) gathers information from various sources and analyzes it to make essential business decisions. ETL tools help extract data from various sources, transform the data into the staging area and load the extracted data into any data warehouse or a target system. Usually, there are steps like extraction, cleaning, transforming, loading, and analyzing the data. The ETL process is helpful for any organization, small or large scale, to analyze data for valuable insights. A simple example of ETL processing is managing sales at a shopping mall.

ETL and Python

Various ETL tools provide data collection and analysis more straightforwardly than others. Some ETL tools provide an end-to-end implementation, while others help create custom ETL processes from the beginning. Therefore, Python provides a wide range of helpful ETL tools helpful in every situation. Users can choose from these tools according to their underlying data requirements.

Features of Tools

The following are some of the main features any ETL tool must have:

  • An ETL tool must have the ability to support various data sources. These sources include SQL, NoSQL, XLS, CSV, JSON, and XML.
  • A good ETL tool should support transforming data using row operations and operations such as sorting, joining, aggregation, and many others.
  • The ETL tool should support connectors for Oracle, Snowflake, MySQL, and various other databases.
  • The ETL tools might include dashboards for visualization, ETL pipeline tracking, distributed processing, and many others.

ETL Tools in Python

Python has become famous for data processing, data science, and data analytics over the past few years. Therefore, it possesses various ETL tools for collecting and analyzing data. The following are some of the Python ETL tools:

Apache Airflow

Airbnb created Apache Airflow, which is an open-source workflow management tool. Airflow creates data ETL pipelines, and create, schedule, and observe workflows. This tool works on the Directed Acyclic Graph (DAG) concept. It has a browser-based dashboard and helps visualize workflows and their tracks. If the user wants to form a complex ETL workflow through chaining independent and existing modules together, Airflow is the best option to choose from a wide range of Python ETL tools. Moreover, Airflow supports a command-line interface (CLI) for task graph operations and a graphical user interface (GUI) for visualization for workflows.


Initially, Spotify created Luigi as a Python-based ETL tool for their workload automation, but it is an open-source tool for the users. Luigi helps in creating complex ETL data pipelines. It can also support handling dependency resolution, visualization of workflows, workflow management, command line integration, failure management, and many others. This tool can track every ETL job through a web dashboard. Moreover, Luigi is beneficial for enterprise solutions. Stripe and Red Hat also use Luigi as their ETL processing tool.


Bonobo is a standard Python ETL tool. It is user-friendly and reliable. Bonobo utilizes the graph concept and helps in supporting the parallel processing of multiple elements in the data pipeline. It also provides a visual interface to track ETL pipeline progress. Bonobo allows data extraction from various sources such as JSON, SQL, CSV, XML, XLS, and many more. The transformation process follows UNIX principles. Bonobo does not require any new API learning and provides users with an easy interface.

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