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Top 7 Python ETL Tools To Learn

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  • Last Updated : 05 Dec, 2021

ETL (extract, transform, load) means extracting data from various sources, transforming that extracted data into a well-organized and readable format via techniques like data aggregation & data normalization, and at last loading (the readable data) into storage systems like data warehouses to gain business insights for better decision-making. Now, there is a very common concern among individuals, “is Python good for ETL?“. You need to know when ETL is coupled with the programming capabilities of Python, it becomes flexible for the organizations to create ETL pipelines that not only manage data of customers and team members well but also move and transform it in accordance with business requirements in a simplified manner.  


Curious to access the list of best python ETL tools that can manage well a set of ETL processes by dealing well with complex schemas of massive amounts of structured or unstructured data available in real-time? If yes, then let’s now take a look at the list mentioned below briefly describing their ability to extract, clean, and load data from multiple sources for better operational resilience and performance-oriented analytics. 

1. Bubbles

Written in Python, the ETL framework of this technologically-interactive tool can smoothly execute data pipelines through meta-data. Besides, with this Python-based ETL tool, you may expect:

  • Data cleansing
  • Data monitoring
  • Data auditing
  • Appropriate information about unknown datasets used in heterogeneous data environments

Via all the features listed above, an ETL developer can now deliver the data without thinking much about how to access it and work with its various types stored and managed by a data store. What else he/she now needs for better management of data quality and best solutions which can speed up the process of data processing?

2. mETL

mETL or Mito-ETL is a lightweight, web-based ETL tool through which developers may create custom coding components that developers (or other responsible employees of an organization) can run, integrate, or download for fulfilling data integration requirements of the organization they are working with. And as per the table of contents of mETL documentation, the tool is good for:

  • RDBMS Data Integrations 
  • API / Service Based Data Integrations
  • Pub / Sub (Queue based) Data Integrations
  • Flat File Data Integrations

To be more specific, Mito-ETL may now be used by developers and programmers for loading any kind of data and then, transforming it through quick transformations and manipulations not demanding some expert or high-level programming skills. 

3. Spark

Spark is an in-demand and useful Python-based tool with which ETL engineers, data scientists can write powerful ETL frameworks very easily. Though it isn’t a Python tool technically, yet through PySpark API, one can easily:

  • do all sorts of data processing.
  • analyze, transform the existing data into formats like JSON via ETL pipeline using Spark.
  • execute implicit data parallelism.
  • continue operating ETL systems with the fault tolerance ability of Spark. 

Thus, with the simplicity of Python strapped by Spark, data engineers, and data scientists can now tame big data with the Extract, Transform, and Load process (or the steps associated) executed analytically by this tool and also, handle unstructured data in variable data warehouse environments. 

4. Petl

Petl or Python ETL is a general-purpose tool for extracting, transforming, and loading various types of tables of data imported from sources like XML, CSV, Text, or JSON. Undoubtedly, with its standard ETL (extract transform load) functionality, you may flexibly apply transformations (on data tables) like sorting, joining, or aggregation.  

Though Petl does not entertain exploratory analysis of complex and larger datasets like categorical data (call it a collection of information in the form of variables divided into categories like age group, sex, race), yet you should consider this simple yet lightweight tool for building a simple ETL pipeline subsequently extracting data from multiple sources. You can conveniently get started with Petl’s documentation and in case, if problems arise during the installation process, do report them on the email address

5. Riko

Riko, an open-source stream processing engine with more than 1K GitHub stars, can analyze and process large streams of unstructured data. In addition, its command-line interface supports:

  • Parallel execution of data streams through synchronous and asynchronous APIs.
  • RSS feeds for publishing blog entries, audio, news headlines.
  • CSV/XML/JSON/HTML files.

Indeed, many of us are not aware of the fact that this open-source Python-based tool is a replacement for Yahoo pipes. This is because just like Yahoo pipelines, the tool supports both asynchronous & synchronous APIs which if integrated with data warehouse systems, can help a lot of ventures to create Business Intelligence Applications interacting as per demand with the databases of customers. 

6. Luigi

Airflow vs Luigi!! The choice of one or both won’t produce non-fruitful results since both solve similar problems by defining tasks and the dependencies associated. But at times, you need to build complex ETL pipelines, this sophisticated tool (Luigi) created by Spotify won’t disappoint you with the tested functionalities like:

  • Command-line integration
  • Workflow management
  • Dependency Resolution
  • A Web-dashboard for tracking ETL jobs and handling failures, if occur

Thinking about how you or your tech-buddies can get started with Luigi!! Try downloading luigi-3.0.3.tar.gz file from its source PyPI for installing its latest, stable version. 

7. Airflow

Airflow, a DAG-based (Directed Acyclic Graphs) open-source platform, is equipped with workflow management capabilities through which you can’t only schedule, but also create and monitor workflows to complete a sequence of tasks. Like other Python-based ETL tools, Airflow can:

  • Create Data ETL pipelines that can usefully extract, transform, and load data into a data warehouse like Oracle, Amazon Redshift. 
  • Visualize Workflow and track their multiple executions too.
  • Monitor, schedule, and organize ETL processes.

In spite of all the above capabilities, Airflow has succeeded well in completing jobs somewhere dependent on dynamic pipeline generation. Thus, ETL developers now need not get worried about how to write well-organized Python codes that can capably instantiate pipelines dynamically.

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