Top 7 Python ETL Tools for Data Engineering

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# Introduction

 
Building Extract, Transform, Load (ETL) pipelines is one of the many responsibilities of a data engineer. While you can build ETL pipelines using pure Python and Pandas, specialized tools handle the complexities of scheduling, error handling, data validation, and scalability much better.

The challenge, however, is knowing which tools to focus on. Some are complex for most use cases, while others lack the features you'll need as your pipelines grow. This article focuses on seven Python-based ETL tools that strike the right balance for the following:

• Workflow orchestration and scheduling
• Lightweight task dependencies
• Modern workflow management
• Asset-based pipeline management
• Large-scale distributed processing

These tools are actively maintained, have strong communities, and are used in production environments. Let's explore them.

# 1. Orchestrating Workflows With Apache Airflow

 
When your ETL jobs grow beyond simple scripts, you need orchestration. Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows, making it the industry standard for data pipeline orchestration.

Here's what makes Airflow useful for data engineers:

• Lets you define workflows as directed acyclic graphs (DAGs) in Python code, giving you full programming flexibility for complex dependencies
• Provides a user interface (UI) for monitoring pipeline execution, investigating failures, and manually triggering tasks when needed
• Includes pre-built operators for common tasks like moving data between databases, calling APIs, and running SQL queries

Marc Lamberti's Airflow tutorials on YouTube are excellent for beginners. Apache Airflow One Shot — Building End To End ETL Pipeline Using AirFlow And Astro by Krish Naik is a helpful resource, too.

# 2. Simplifying Pipelines With Luigi

 
Sometimes Airflow feels like overkill for simpler pipelines. Luigi is a Python library developed by Spotify for building complex pipelines of batch jobs, offering a lighter-weight alternative with a focus on long-running batch processes.

What makes Luigi worth considering:

• Uses a simple, class-based approach where each task is a Python class with requires, output, and run methods
• Handles dependency resolution automatically and provides built-in support for various targets like local files, Hadoop Distributed File System (HDFS), and databases
• Easier to set up and maintain for smaller teams

Check out Building Data Pipelines Part 1: Airbnb's Airflow vs. Spotify's Luigi for an overview. Building workflows — Luigi documentation contains example pipelines for common use cases.

# 3. Streamlining Workflows With Prefect

 
Airflow is powerful but can be heavy for simpler use cases. Prefect is a modern workflow orchestration tool that's easier to learn and more Pythonic, while still handling production-scale pipelines.

What makes Prefect worth exploring:

• Uses standard Python functions with simple decorators to define tasks, making it more intuitive than Airflow's operator-based approach
• Provides better error handling and automatic retries out of the box, with clear visibility into what went wrong and where
• Offers both a cloud-hosted option and self-hosted deployment, giving you flexibility as your needs evolve

Prefect’s How-to Guides and Examples should be great references. The Prefect YouTube channel has regular tutorials and best practices from the core team.

# 4. Centering Data Assets With Dagster

 
While traditional orchestrators focus on tasks, Dagster takes a data-centric approach by treating data assets as first-class citizens. It's a modern data orchestrator that emphasizes testing, observability, and development experience.

Here’s a list of Dagster’s features:

• Uses a declarative approach where you define assets and their dependencies, making data lineage clear and pipelines easier to reason about
• Provides excellent local development experience with built-in testing tools and a powerful UI for exploring pipelines during development
• Offers software-defined assets that make it easy to understand what data exists, how it's produced, and when it was last updated

Dagster basics tutorial walks through building data pipelines with assets. You can also check out Dagster University to explore courses that cover practical patterns for production pipelines.

# 5. Scaling Data Processing With PySpark

 
Batch processing large datasets requires distributed computing capabilities. PySpark is the Python API for Apache Spark, providing a framework for processing massive amounts of data across clusters.

Features that make PySpark essential for data engineers:

• Handles datasets that don't fit on a single machine by distributing processing across multiple nodes automatically
• Provides high-level APIs for common ETL operations like joins, aggregations, and transformations that optimize execution plans
• Supports both batch and streaming workloads, letting you use the same codebase for real-time and historical data processing

How to Use the Transform Pattern in PySpark for Modular and Maintainable ETL is a good hands-on guide. You can also check the official Tutorials — PySpark documentation for detailed guides.

# 6. Transitioning To Production With Mage AI

 
Modern data engineering needs tools that balance simplicity with power. Mage AI is a modern data pipeline tool that combines the ease of notebooks with production-ready orchestration, making it easier to go from prototype to production.

Here's why Mage AI is gaining traction:

• Provides an interactive notebook interface for building pipelines, letting you develop and test transformations interactively before scheduling
• Includes built-in blocks for common sources and destinations, reducing boilerplate code for data extraction and loading
• Offers a clean UI for monitoring pipelines, debugging failures, and managing scheduled runs without complex configuration

The Mage AI quickstart guide with examples is a great place to start. You can also check the Mage Guides page for more detailed examples.

# 7. Standardizing Projects With Kedro

 
Moving from notebooks to production-ready pipelines is challenging. Kedro is a Python framework that brings software engineering best practices to data engineering. It provides structure and standards for building maintainable pipelines.

What makes Kedro useful:

• Enforces a standardized project structure with separation of concerns, making your pipelines easier to test, maintain, and collaborate on
• Provides built-in data catalog functionality that manages data loading and saving, abstracting away file paths and connection details
• Integrates well with orchestrators like Airflow and Prefect, letting you develop locally with Kedro then deploy with your preferred orchestration tool

The official Kedro tutorials and concepts guide should help you get started with project setup and pipeline development.

# Wrapping Up

 
These tools all help build ETL pipelines, each addressing different needs across orchestration, transformation, scalability, and production readiness. There is no single "best" option, as each tool is designed to solve a particular class of problems.

The right choice depends on your use case, data size, team maturity, and operational complexity. Simpler pipelines benefit from lightweight solutions, while larger or more critical systems require stronger structure, scalability, and testing support.

The most effective way to learn ETL is by building real pipelines. Start with a basic ETL workflow, implement it using different tools, and compare how each approaches dependencies, configuration, and execution. For deeper learning, combine hands-on practice with courses and real-world engineering articles. Happy pipeline building!
 
 

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she's working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.