Extract, transform, load (ETL) is a critical component of data warehousing, as it enables efficient data transfer between systems. In the current scenario, Python is considered the most popular language for ETL. There are numerous Python-based ETL tools available in the market, which can be used to define data warehouse workflows. However, choosing the right ETL tool or your needs can be a daunting task. You can choose from a wide range of ETL solutions, including those written in JavaScript, Java, Apache Hadoop, and Go. But Python is the most widely used language in the ETL space. It is a high-level and general-purpose programming language that is utilized by many of the world's largest brands.

In 2024, there will be more than a hundred Python tools available for ETL, including frameworks, libraries, and software. Six of the top Python ETL tools are listed below, but keep in mind that you'll need to have a basic knowledge of Python to use them. On average, it takes 8 weeks to learn the basics of Python.

However, if you don't have time to learn Python or you're looking for a more straightforward alternative, Integrate.io can be a good option. Integrate.io is a cloud-based, no-code solution that is used by Fortune 500 companies to extract, transform, and load data for business intelligence and analytics. Since it doesn't require any coding, you don't need to learn Python to use it. More information on Integrate.io will be provided later.

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Why Use Python for ETL Pipelines in 2024?

Python is a versatile programming language that is widely used for ETL pipelines in 2024. There are many reasons why organizations choose to set up ETL pipelines with Python. One of the main reasons is that Python is well-suited for dealing with complex schemas and large amounts of big data, making them the better choice for data-driven organizations. Sure, you can use something like SQLAlchemy to execute ETL in Python. But this process is time-consuming and overwhelming. Additionally, Python has a large and active community of developers who are constantly creating and updating ETL tools and libraries.

While it is possible to use an ETL tool to set up ETL pipelines, Python offers more flexibility and customization options. With Python, you can build an ETL tool that is tailored to your specific needs. Also, Python allows you to perform simple ETL tasks with minimal coding, which can be useful for small-scale projects.

There are only three situations where Python makes sense for ETL:

  • You have experience with Python and want to build an ETL tool from scratch.
  • You have simple ETL requirements and want a lightweight solution.
  • You have a unique need that can only be met by custom coding an ETL solution using Python.

There are many Python-based ETL tools available in 2024, and the six top Python ETL tools are listed below. These tools have been selected based on their usability, popularity, and diversity. However, it's worth noting that a basic knowledge of Python is required to use these tools. If you don't know Python or don't want to code pipelines from scratch, you may want to consider using Integrate.io’s features.  

Recommended Reading: Other ETL Tools, Libraries, and Frameworks

1. Apache Airflow for Python-Based Workflows

Apache Airflow is an open-source, Python-based workflow automation tool that is used for setting up and maintaining powerful data pipelines. It is not an ETL tool, per se, but it manages, structures, and organizes ETL pipelines using Directed Acyclic Graphs (DAGs). DAGs are used to form relationships and dependencies between tasks, allowing for the running of a single branch multiple times and skipping branches from sequences when necessary.

For example, you can make task A run after task B and make task C run every 2 minutes. Or make task A and task B run every 2 minutes and make task C run after task B.

The typical architecture of Apache Airflow includes:

- Metadata database > scheduler > executor > workers

The metadata database stores workflows/tasks (DAGs), the scheduler (typically run as a service) uses DAG definitions to select tasks, the executor determines which worker executes tasks, and workers are the processes that execute the logic of workflows/tasks.

Apache Airflow is a valuable addition to your toolbox because it's useful for management and organization. It also has hooks and operators for Google Cloud and AWS, making it useful for cloud warehousing environments. However, it is worth noting that Apache Airflow is not a library, so it needs to be deployed, which may not be practical for small ETL jobs.

Apache Airflow makes the most sense when you're performing long ETL jobs or when ETL has multiple steps.

Facts About Apache Airflow:

  • It won InfoWorld's Best of Open Source Software Award in 2020.
  • The majority of Airflow users leverage Celery to simplify execution management.
  • You can schedule automated DAG workflows via the Airflow WebUI.
  • Airflow uses a command-line interface, which is extremely useful for isolating execution tasks outside of scheduler workflows.
  • Prefect is a solid alternative to Airflow with advanced features, and you can migrate DAGs straight over.

As you can see, executing Apache Airflow can be complicated. Integrate.io provides a no-code alternative to Python, automating complicated data pipelines. 

2. Luigi for Complex Python Pipelines

Luigi is an open-source, Python-based tool that is used for building complex pipelines. Developed by Spotify to automate heavy workloads, Luigi is used by data-driven corporations such as Stripe and Red Hat.

There are three main benefits to using Luigi:

  • Dependency management with excellent visualization
  • Failure recovery through the use of checkpoints
  • Command-line interface integration

The primary difference between Luigi and Airflow is the way these top Python ETL tools execute tasks and dependencies. In Luigi, you'll find "tasks" and "targets," and tasks consume targets. This target-based approach is perfect for simple Python-based ETL, but Luigi may struggle with highly complex tasks.

Luigi is best suited for automating simple ETL processes, such as logs, as it can handle them quickly and with minimal setup. However, its strict pipeline-like structure limits its ability to handle complex tasks. Additionally, even simple ETL processes require a certain level of Python coding skills.

Facts about Luigi:

  • Once Luigi runs tasks, you cannot interact with the processes.
  • Unlike Airflow, Luigi does not automatically schedule, alert, monitor, or sync tasks to workers.
  • The lack of a user-friendly interface is a pain point of Luigi
  • According to Towards Data Science, Luigi uses a "target-based approach," but its UI is "minimal," with no user interaction for running processes.

In short, Luigi is a powerful Python-based tool that can automate simple ETL tasks, but it lacks the flexibility and scalability of other ETL tools like Apache Airflow or Integrate.io. 

Recommended Reading: Building an ETL Pipeline in Python

3. pandas for Data Structures and Analysis Tools

If you've been working with any top Python ETL tools for a while, you might know about pandas. pandas is a widely used open-source library that provides data structures and analysis tools for Python. It is particularly useful for ETL tasks, as it adds R-style data frames that make ETL processes like data cleansing and transformation easier. With pandas, you can easily set up simple scripts to load data from various sources, clean and transform the data, and write the data to a variety of formats such as Excel, CSV, or SQL databases.

However, it's worth noting that pandas may not be the best choice for large scale data processing and in-memory operations. While it is possible to scale pandas using parallel chunks, it is not as simple as using other top Python ETL tools such as Apache Airflow.

When Does pandas Make Sense?

pandas is ideal for ETL tasks when working with small to medium-sized datasets and when the primary focus is on data cleansing, transformation, and manipulation. It is particularly useful when extracting data, cleaning it, transforming it, and writing it to Excel, a CSV file, or an SQL database. However, for large scale data processing and in-memory operations, it is recommended to use other more specialized tools.

Facts About pandas:

  • NumFocus sponsors pandas.
  • Many Python users choose pandas for ETL batch processing.
  • pandas boasts a high rating of 4.5/5 on G2.com. (That's higher than Airflow.) Users say it's "powerful," "very practical," but there’s "a learning curve."

4. petl as a Python ETL Solution

In general, petl is among the most straightforward top Python ETL tools. It is a widely used open-source Python ETL tool that simplifies the process of building tables, extracting data from various sources, and performing various ETL tasks. It is similar in functionality to pandas, but without the same level of data analysis capabilities.

petl is known for its ability to handle complex datasets efficiently, utilizing system memory and providing reliable scalability. However, it is not as fast as other ETL tools in the market. It is still considered an easy to use option compared to building ETL using SQLAlchemy or other custom-coded solutions.

When Does petl Make Sense?

petl is a good choice when you need basic ETL functionality without the need for advanced analytics, and speed is not a critical factor.

Facts About petl:

  • petl is not known for its speed or handling of large datasets.
  • petl stands for "Python ETL."
  • petl is widely acknowledged as a basic and trivial tool, but is praised for its support of standard transformations like sorting, joining, aggregation, and row operation.

5. Bonobo as a Lightweight Python ETL Framework

Bonobo is a lightweight and easy-to-use Python ETL framework that allows for rapid deployment of data pipelines and parallel execution. It supports a wide range of data sources, including CSV, JSON, XML, XLS, and SQL, and adheres to atomic UNIX principles. One of the main benefits of using Bonobo is that it requires minimal learning of a new API, making it accessible to those with a basic knowledge of Python. It can be used to build graphs, create libraries, and automate simple ETL batch processes. Despite its simplicity, Bonobo is open-source, scalable and can handle semi-complex data schemas.

When Does Bonobo Make Sense?

Bonobo is ideal for simple and lightweight ETL jobs, and for those who do not have the time or resources to learn a new API. However, it still requires a basic understanding of Python, so for those looking for a no-code solution, Integrate.io might be a better option

Facts About Bonobo:

  • Bonobo offers a Docker extension that allows for running jobs within Docker containers.
  • It has a command-line interface (CLI) for execution.
  • Bonobo has built-in Graphviz support for visualizing ETL job graphs.
  • It also has an SQLAlchemy extension (currently in alpha)
  • Users have found the experience of writing their first ETL in Python with Bonobo to be simple and straightforward.

6. Bubbles as a Python Framework for ETL

Bubbles is a versatile Python framework that simplifies ETL processes. Unlike other top Python ETL tools, Bubbles utilizes metadata to describe pipelines, and can be used for various data integration, data cleansing, data auditing, and more.

Although written in Python, Bubbles is not exclusively limited to it, and can be used with other languages.

One of the key benefits of Bubbles is its technological agnosticism, which allows users to focus solely on ETL processes without worrying about the underlying technology or data access.

When Does Bubbles Make Sense?

Bubbles is a great choice for those who need a rapid ETL setup and want the freedom to focus solely on ETL processes without being limited by the underlying technology.

Facts About Bubbles:

  • Bubbles is an abstract framework with a focus on ETL rather than learning a specific programming language.
  • According to Open Knowledge Labs, "Bubbles is, or rather is meant to be, a framework for ETL written in Python, but not necessarily meant to be used from Python only."

Other ETL Tools, Libraries & Frameworks

There are simply too many top Python ETL tools to include in this post. We tried to keep our list simple by including popular ETL options that have different use cases. But there are plenty of other tools to mention. 

In this section, we’ll list other ETL platforms by language.


  • BeautifulSoup: This example of top Python ETL tools pulls data out of webpages (XML, HTML) and integrates with ETL tools like petl.
  • PyQuery: Also extracts data from webpages, but with a jquery-like syntax.
  • Blaze: This is an interface that queries data. Part of the "Blaze Ecosystem," a framework for ETL that uses Blaze, Dask, Datashape, DyND, and Odo.
  • Dask: Use Dask for Parallel computing via task scheduling. It also processes continuous data streams and is part of the "Blaze Ecosystem."
  • Datashape: A simple data-description language similar to NumPly. It describes in-situ structured data with no canonical transformation.
  • DyND: The Python exposure for DyND — a C++ library for dynamic, multidimensional arrays.
  • Odo: Move data between multiple containers. Odo uses the native CSV loading capabilities of SQL databases, making it faster than loading with Python.
  • Joblib: More tools that use Python functions for pipelines, but Joblib has a few unique perks that make it suitable for certain jobs such as NumPy arrays.
  • lxml: Processes HTML and XML in Python.
  • Retrying: Lets you add retry behavior to executions.
  • riko: A Yahoo! Pipes replacement that's useful for stream data. Not a full-blown ETL solution, but it's pure Python and makes extracting data streams easier.


  • Integrate.io: Do you want to set up automated pipelines across a variety of sources using best-of-breed visualization and rapid integrations? You need Integrate.io! Point-and-click, 200+ out-of-the-box integrations, Salesforce-to-Salesforce integration, and more. No code is required whatsoever. The only solution for ETL.
  • AWS Data Pipeline: Amazon's data pipeline solution. Set up pipelines between AWS instances and legacy servers.
  • AWS Glue: Amazon's fully managed ETL solution. Manage it directly through the AWS Management Console.
  • AWS Batch: Used for batch computing jobs on AWS resources. It has good scalability that suits engineers on large jobs.
  • Google Dataflow: Google's ETL solution for batch jobs and streams.
  • Azure Data Factory: Microsoft's ETL solution.


  • Toil: This USCS project handles ETL almost identically to Luigi. (Use Luigi to wrap Toil pipelines for additional checkpointing.)
  • Pachyderm: Another alternative to tools like Airflow. Here's a great GitHub writeup about some of the simple differences between Airflow and Pachyderm. (Pro tip: Pachyderm  has an open-source edition on its website.)
  • Mara: Yep, another ETL framework that uses Python's capabilities. It sits somewhere between pure Python and Apache Airflow, and it's fast and simple to set up.
  • Pinball: Pinterest's workflow manager with auto-retries, priorities, overrun policies, and horizontal scalability.
  • Azkaban: Created by LinkedIn, Azkaban is a Java-based tool for Hadoop batches. But if you're trying to run hyper-complex Hadoop batches, think about Oozie instead.
  • Dray.it: A Docker workflow engine that helps with resource management.
  • Spark: Set up your entire batch streaming ETL.

Other Languages

We won't dive too deep into the tools below. But here's a list of ETL platforms outside of the Python space.



JavaScript (Node.js)

The Better ETL Solution

The top Python ETL tools on this list prove useful for less-complex jobs. But most growing enterprises need a speedier, scalable solution that leverages multiple tool layers for comprehensive ETL.

Integrate.io is a no-code, cloud-based solution that builds robust pipelines in minutes, with 200+ integrations that make ETL a piece of cake. It requires no data engineering experience and has limitless features. Most top Python ETL tools move data from Salesforce to a warehouse, but Integrate.io moves it back again. Then there's the super-simple workflow creation for defining dependencies between tasks, easy data transformations, a reliable REST API enhanced data security and compliance, and free customer support for all users. It's the only ETL solution you'll need.

Forget about Python-based tools in 2024. You need a no-code ETL solution that lets you extract, transform, and load data without having to start from scratch. Integrate.io is pain-free but sturdy enough to handle even the most demanding of workloads.

Integrate.io is the future of ETL. No code. No messing around. It's data transformation on your terms.

 Click here to schedule an intro call with our support team to see if Integrate.io is the right fit for your team!