Top 9 Python ETL Tools to Consider in 2024 – Ranked by Popularity

Python has been dominating the ETL space for a few years now. There are easily more than a hundred Python ETL Tools that act as Frameworks, Libraries, or Software for ETL. Why so? Because it’s versatile, easy to use, and has a rich ecosystem of libraries and tools.

Python also has a large community to help you out with ETL in Python. Its quick development cycle enables you to rapid prototyping of ETL processes.

Now, the question is, do you want to build pipelines in-house or adopt an ETL using Python? I will leave that to you. You can decide after weighing the pros and cons of the ETL tools in this blog.

Python ETL: A Powerful Framework for Data Integration

Python has emerged as a dominant force in the field of data engineering, particularly for Extract, Transform, Load (ETL) processes. This powerful combination leverages the flexibility and versatility of Python to efficiently collect, clean, and move data across various sources.

Key Advantages of Python ETL:

• AI/ML Integration: Python’s seamless integration with machine learning libraries (e.g., scikit-learn, TensorFlow) enables AI-powered data transformations, anomaly detection, and predictive modeling within the ETL pipeline.
• Scalability and Efficiency: Python libraries like Pandas and Dask provide high-performance data manipulation and processing capabilities, handling large datasets with ease.
• Open-Source Ecosystem: A vast array of open-source libraries and tools are readily available, catering to diverse ETL requirements.
• Community Support: A large and active Python community ensures continuous development, support, and access to valuable resources.

Popular Python ETL Tools

Apache Airflow

• Apache Airflow is an Open Source automation Tool built on Python used to set up and maintain Data Pipelines.
• Technically, Airflow is not an ETL Tool but rather lets you organize and manage your ETL Pipelines using DAGs (Directed Acyclic Graphs).
• DAGs lets you run a single branch more than once or even skip branches in your sequence when necessary.
• A typical Airflow setup will look something like this: Metadata database > Scheduler > Executor > Workers
• The Metadata Database stores your workflows/tasks, the Scheduler, which runs as a service uses DAG definitions to choose tasks and the Executor decides which worker executes the task. Workers execute the logic of your workflow/task.
• Apache Airflow can seamlessly integrate with your existing ETL toolbox since it’s incredibly useful for Management and Organization. Apache Airflow makes sense when you want to perform long ETL jobs or your ETL has multiple steps, Airflow lets you restart from any point during the ETL process. However, it should be clear that Apache Airflows isn’t a library, so it needs to be deployed and therefore, may not be suitable for small ETL jobs.
• One key element of Airflow is that you can easily manage all of DAG’s workflows via the Airflow WebUI. This means that you can schedule automated workflows without having to manage and maintain them. You will also be able to execute it using a Command-Line Interface.

Luigi

• Luigi is also an Open Source Python ETL Tool that enables you to develop complex Pipelines. It has a number of benefits which include good Visualization Tools, Failure Recovery via Checkpoints, and a Command-Line Interface.
• The main difference between Luigi and Airflow is in the way the Dependencies are specified and the Tasks are executed. Luigi works with Tasks and Targets. 
• Tasks utilize the Targets, which are produced by a finished Task. So, a Task will remove a Target, then another Task will consume that Target and remove another one. This allows the whole process to be straightforward, and workflows to be simple. This is right for simple ETL Tasks but not complex Tasks.
• Luigi is your best choice if you want to automate simple ETL processes like Logging. It is important to note that with Luigi you cannot interact with the different processes. Also, Luigi does not automatically sync Tasks to workers for you. It does not provide the facility to Schedule, Alert or Monitor as Airflow would. 

Pandas

• Pandas is a Python library that provides you with Data Structures and Analysis Tools. It simplifies ETL processes like Data Cleansing by adding R-style Data Frames. However, it is time-consuming as you would have to write your own code. It can be used to write simple scripts easily, and is one of the widely used Python ETL tools.
• However, when it comes to in-memory and scalability, Pandas’ performance may not keep up with expectations.
• You should use Pandas when you need to rapidly extract data, clean and transform it, and write it to an SQL Database/Excel/CSV. Once you start working with large data sets, it usually makes more sense to use a more scalable approach.

Bonobo

• Bonobo is lightweight and easy to use. You will be able to deploy pipelines rapidly and in parallel. Bonobo can be used to extract data from multiple sources in different formats including CSV, JSON, XML, XLS, SQL, etc.
• This transformation follows atomic UNIX principles. One of the best qualities about Bonobos is that new users will not have to learn a new API.
• It is especially easy to use if you have a background in Python. It also has the ability to handle semi-complex schemas. One of the biggest plus points is that it’s Open Source and scalable.
• Bonobo is suitable when you need simple, lightweight ETL jobs done, and you don’t have the time to learn a new API. One more key point to note is that Bonobo has an official Docker that lets you run jobs within Docker Containers. Moreover, it allows CLI execution as well.

petl

• petl is an aptly named Python ETL solution. You can extract data from multiple sources and build tables. It is quite similar to Pandas in the way it works, although it doesn’t quite provide the same level of analysis.
• petl is able to handle very complex datasets, leverage system memory, and can scale easily too. The best use case for using petl is when you want the basics of ETL without the analytics and the job is not time-sensitive.

PySpark

• Among all the Python ETL tools, PySpark has one of the most versatile interfaces designed for Apache Spark that allows users to use Python APIs to write Spark applications.
• It is needed because Apache Spark is written in Scala language, and to work with Apache Spark using Python, an interface like PySpark is required.
• PySpark helps users connect with Resilient Distributed Datasets (RDDs) to Apache Spark and Python.
• PySpark supports most of Apache Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core. 

Odo

• Odo is a Python tool that converts data from one format to another and delivers high performance while loading huge datasets into various datasets.
• It includes in-memory structures like NumPy array, data frames, lists, etc. Users should try Odo if they looking to make simple pipelines but want to load large CSV datasets.
• It also supports data outside of Python like CSV/JSON/HDF5 files, SQL databases, data on remote machines, and the Hadoop File System.

mETL

• mETL is a Python ETL tool that is designed for loading elective data for CEU. It is a web-based ETL tool that allows developers to create custom components that they can run and integrate as per the data integration requirements by an organization.
• It can load any kind of data and comes with widespread file formats with data migration and data migration packages.
• Users can use mETL for service-based data integrations, flat-file integrations, publisher-subscriber data integrations, etc.

Riko

• Riko is a stream processing engine written in Python to analyze and process streams of structured data.
• Riko is best suited for handling RSS feeds as it supports parallel execution using its synchronous and asynchronous APIs.
• It also comes with CLI support for the execution of stream processors. It is modeled after Yahoo pipes and became its replacement and can help a lot of companies to create Business Intelligence Applications interacting as per demand with the databases of customers when connected with Data Warehouses.

How to Use Python for ETL?

Python is a versatile language that users can use to code almost any ETL process. It can easily handle indexed data structures and dictionaries, which is important in ETL operations.

For instance, using some built-in Python modules makes it really easy to filter out null values from a list:

import math
data = [1.0, 3.0, 6.5, float('NaN'), 40.0, float('NaN')]
filtered = []
for value in data:
if not math.isnan(value):
    filtered.append(value)

You can use list comprehensions to accomplish this task as well:

filtered = [value for value in data if not math.isnan(value)]

It is not very efficient to code the entire ETL process from scratch, as most of the ETL code becomes a mix of pure Python script and externally defined objects or functions. For example, you can use pandas to filter out entire data frames of rows containing null values:

filtered = data.dropna()

Software Development kits of Python, APIs, and other supports are available for easy development in Python that are highly useful in building ETL Tools

How to Select the Best Python ETL Tool

When selecting the best Python ETL tool for your data engineering
projects, choose one that:

• Covers all of the numerous data sources from which raw data can be extracted.
• Can handle sophisticated pipelines for cleaning and converting data.
• Covers all data destinations (SQL databases, data warehouses, data lakes, and filesystems) to which you will load your data.
• It can easily scale if numerous jobs are running simultaneously to save time.
• Is extensible – it can be used not just for data engineering but also by data scientists to develop complicated schemas for data science projects.
• Are easily monitored; observability is critical for debugging and ensuring data quality.

Best Practices for Python ETL:

• Leverage AI/ML: Integrate machine learning models for data enrichment, anomaly detection, and feature engineering within the ETL pipeline.
• Write Clean and Maintainable Code: Adhere to Pythonic principles for readability and maintainability.
• Document Thoroughly: Provide clear and concise documentation for all scripts and functions.
• Manage Dependencies Effectively: Utilize tools like pipenv or poetry to manage project dependencies and ensure reproducibility.

Additional Reads:

• Python Batch Processing: The Best Guide
• Python Webhook Integration: 3 Easy Steps
• Understanding Data Modelling in Python: 4 Critical Aspects

In-Depth Analyses of Popular Open Source Tools

• Open Source ETL Tools
• PostgreSQL ETL Tools
• Top 12 Open Source Ci Cd Tools
• Top 10 Tableau Open Source
• 5 Best Open Source Data Replication Tools
• 13 Best Data Ingestion Open Source
• MongoDB Open Source
• Snowflake Open Source
• NodeJS Open source ETL tools

Conclusion

In this blog post, you have seen the 9 most popular Python ETL tools available in the market. The Python ETL tools you choose depend on your business needs, time constraints, and budget. The Python ETL tools we discussed are Open Source and thus can be easily leveraged for your ETL needs. 

Designing a custom pipeline using the Python ETL Tools is often a time-consuming & resource intensive task. This requires you to assign a portion of your engineering bandwidth to design, develop, monitor & maintain data pipelines for a seamless data replication process.

If you’re looking for a more effective all-in-one solution, that will not only help you transfer data but also transform it into analysis-ready form, then a Cloud-Based ETL Tool like Hevo Data is the right choice for you!

You can also have a look at the unbeatable Hevo Pricing that will help you choose the right plan for your business needs.

Have any further questions? Get in touch with us in the comments section below.

FAQs on Python ETL