9 Python ETL Tools Compared for Orchestration, Batch, and Streaming Pipelines

Python is one of the most widely used languages for building ETL pipelines, but no single Python tool fits every data engineering workflow. Some tools focus on orchestration, others on distributed batch processing, and a smaller set supports real-time or streaming data processing. Choosing the right Python ETL tool depends on data volume, workflow complexity, and how frequently data needs to move.

This article compares nine widely used Python ETL tools and frameworks, explaining what each one is best suited for and where it falls short. You’ll learn how these tools differ across batch processing, orchestration, and real-time streaming, and how to combine them effectively to build scalable, maintainable data pipelines.

Suggested Read: ETL Tools

Python ETL Tools: Comparison of Capabilities and Use Cases

Here’s a side-by-side comparison of the best Python ETL tools based on scalability, real-time processing, complexity, and ideal use cases. Use this table to quickly find the tool that fits your needs before exploring the details below.

In this article, “real-time” refers to continuous or micro-batch stream processing, not simply scheduling batch jobs more frequently.

Now that you’ve compared these Python ETL tools side by side, let’s explore each one in detail to find the best fit for your workflow.

9 Best Python ETL Tools

Here are nine widely used Python ETL tools and frameworks that cover orchestration, batch processing, and real-time streaming. These tools fall into three categories: orchestration, batch transformation engines, and real-time stream processing.

1. Apache Airflow

Apache Airflow is one of the most widely used Python ETL tools for orchestrating and scheduling complex workflows. It creates ETL pipelines using a Directed Acyclic Graph (DAG). In these pipelines, tasks run based on defined dependencies, so downstream steps only execute when upstream requirements are met. This ensures that tasks run in the right order. Airflow handles many tasks, like extracting, transforming, and loading data into different systems.

Key Features:

• Orchestration of Complex Workflows: Airflow’s DAG system lets you build workflows where tasks depend on each other. Each task can be a Python script or another type of operator.
• Monitoring and Logging: Airflow provides a web-based UI to track the progress of workflows. You can easily monitor task status, check for failures, and rerun jobs if needed.
• Scalability: Airflow scales well, managing both small and large data processing pipelines. It's suitable for enterprise-level data needs.
• Extensibility and Flexibility: Airflow works with cloud platforms, databases, and APIs. This makes it useful for many ETL tasks, like loading data into cloud storage or processing large datasets across clusters.

2. Luigi

Luigi, created by Spotify, is a Python ETL tool designed to manage complex workflows. Similarly to Airflow, It handles pipelines where tasks depend on each other, ensuring that each task runs only after the previous one finishes. This makes Luigi great for managing large workflows with many connected tasks.

Key Features:

• Task Dependency Management: Luigi handles workflows with task dependencies effectively. Each task is part of a broader pipeline, and Luigi ensures that the tasks follow the correct execution order.
• Task Progress Visualization: Luigi makes it easy to track task progress with visual tools. These tools help data engineers monitor tasks, spot errors, and rerun any failed steps.
• Integration Capabilities: Luigi works well with systems like Hadoop, Spark, and SQL databases.
• Simplified Task Execution: Luigi breaks down tasks into smaller, modular components. This makes it easier to test, troubleshoot, and maintain workflows.

3. PySpark

PySpark is a powerful Python ETL tool. Apache Spark, its underlying compute engine, is designed to process large datasets using distributed computing. PySpark is the Python API for Spark. It lets you use Spark’s data processing power in Python. This makes it perfect for big data tasks. PySpark handles large datasets efficiently. It also integrates well with machine learning libraries, making it ideal for advanced workflows.

Key Features:

• Distributed Data Processing: PySpark divides large datasets into smaller parts. It processes them at the same time, which makes data processing much faster.
• Big Data Processing: PySpark supports both RDDs and the higher-level DataFrame API, which is commonly used for ETL and analytics at scale. RDDs split data across multiple nodes, making processing faster and ensuring that any failures don’t stop the entire workflow.
• Integration with Machine Learning: PySpark works with Spark’s MLlib, a machine learning library. This makes it a good fit for ETL tasks that involve machine learning.
• SQL-Like Operations: With PySpark’s DataFrame API, you can perform SQL-like queries on large datasets. This makes transforming and querying data easier and faster.

4. Polars

Polars is a lightning-fast DataFrame library in Rust with a Python API, making it an efficient choice for data manipulation and ETL processes. Polars provides a memory-efficient approach with multi-threaded operations, making it perfect for processing large datasets in a fraction of the time. With its DataFrame API, it supports many SQL-like operations and excels at handling complex transformations.

Polars is primarily designed for single-node performance (multicore), making it ideal for fast local and mid-sized ETL workloads.

Key Features:

• Speed and Efficiency: Polars is optimized for speed, using Rust to handle data operations faster than traditional tools. It leverages multi-threading, allowing for high performance even on large datasets.
• Low Memory Usage: Polars has an efficient memory footprint, ideal for managing large datasets without overwhelming memory.
• Lazy Evaluation: Polars processes data in a lazy manner, optimizing computations to reduce processing time and resource usage.
• Flexible and Feature-Rich API: It includes SQL-like queries, joins, aggregations, and more, making complex data manipulation straightforward.

5. petl

petl is a lightweight Python library used for data extraction, transformation, and loading tasks. It works well for table-based ETL where memory efficiency matters, especially when processing data in a streaming or lazy way. petl only processes data when needed, making it great for simple ETL tasks that have limited resources.

While it doesn’t have advanced features like other ETL frameworks, petl is fast and effective for table manipulation. It’s perfect for users who need to prioritize low memory usage.

Key Features:

• Lazy Loading: petl processes data only when needed. This helps it manage large datasets without using too much memory.
• Low Memory Usage: petl uses less memory. It works well with large datasets and doesn’t need complex systems.
• Extendable: petl can be expanded to support different data formats. This makes it adaptable to various ETL tasks.
• Simple API for Table Manipulation: petl provides a simple API to filter, sort, and transform data. It works well for basic ETL tasks involving structured data. You can use it without the need for large frameworks.

6. Bonobo

Bonobo is a simple and lightweight Python ETL framework. It helps developers build ETL pipelines with reusable components. Bonobo’s modular design makes it perfect for smaller ETL tasks. It also supports parallel processing to boost performance. Although it lacks the complexity of bigger ETL tools, Bonobo is great for simple and maintainable workflows. It is an excellent choice for developers who need something easy to read and test.

Key Features:

• Atomic Pipeline Design: Bonobo breaks each pipeline into smaller, reusable parts, making it easier to test and maintain.
• Parallel Processing: Bonobo can run multiple tasks at the same time, improving performance.
• Flexibility: It comes with built-in connectors for common tasks. You can also customize it for more complex workflows.
• Ease of Use: Bonobo's simple API lets developers build ETL pipelines with minimal effort. It’s a good option for smaller projects or proof-of-concept pipelines.

7. Dagster

Dagster is a modern Python-based data orchestration and ETL framework designed around data assets rather than tasks. It focuses on observability, data lineage, and reliability, making it a popular choice for building maintainable ETL pipelines in production environments.

Unlike traditional task-based orchestrators, Dagster treats datasets as first-class objects. This allows data engineers to reason about pipeline dependencies, freshness, and failures more intuitively. Dagster integrates well with Python ETL tools such as pandas, PySpark, and dbt, making it suitable for both batch processing and complex data workflows.

Key Features

• Asset-Based Orchestration: Pipelines are built around data assets instead of tasks, improving clarity, lineage tracking, and dependency management.
• Strong Observability and Debugging: Dagster provides rich metadata, logs, and UI-based inspection to diagnose pipeline failures and understand data flow.
• Python-Native Design: Works seamlessly with Python libraries like pandas, PySpark, and SQLAlchemy, enabling flexible ETL logic.
• Production-Ready Pipelines: Supports retries, backfills, versioning, and environment-aware deployments for reliable ETL operations.
• Growing Ecosystem: Integrates with cloud platforms, data warehouses, and modern data tools, making it a strong choice for 2026 workflows.

8. dltHub

dltHub is an open-source Python library designed to simplify data extraction, transformation, and loading tasks with a focus on ease of use and scalability. dltHub enables data engineers to create low-code ETL pipelines that can ingest data from multiple sources like APIs, databases, or files and then load it into data warehouses or lakes. With automation for error handling and retry mechanisms, it’s an excellent choice for reliable and efficient data pipelines.

Key Features:

• Incremental Data Loading: dltHub supports incremental loading, updating only new or modified data to save processing time and resources.
• Built-in Error Handling and Retry Mechanisms: Automates retries and error handling, ensuring robust and reliable pipeline execution.
• Broad Integration Support: It is compatible with destinations such as BigQuery, Snowflake, and PostgreSQL, making it versatile for various data storage needs.
• Low-Code, Developer-Friendly API: dltHub's simple API reduces setup complexity, allowing data engineers to deploy and maintain ETL workflows quickly.

9. Bytewax

Bytewax is a Python-based stream processing framework designed for building scalable data pipelines that handle real-time data. Its event-driven architecture allows developers to process streaming data from various sources, making it an ideal choice for real-time analytics, ETL processes, and event-driven systems. Bytewax enables stateful computations on streaming data, allowing for complex transformations and processing.

Key Features:

• Stream Processing: Bytewax excels at processing continuous data streams, making it perfect for real-time ETL workflows.
• Scalable Architecture: Designed for distributed processing, Bytewax can scale across clusters to efficiently handle large volumes of data.
• Stateful Processing: It allows users to maintain state across data events, enabling advanced use cases like sessionization and real-time anomaly detection.
• Integration with Data Sources: Bytewax integrates with various sources like Kafka, WebSockets, and databases, making it versatile for different ETL tasks.
• Pythonic API: Bytewax provides an easy-to-use API, making it accessible to Python developers who want to build complex real-time workflows without learning a new framework.

How to Choose the Best ETL Tools for Python

Choosing the right Python ETL tool depends on how data moves, how much data you process, and how complex your workflows are. Not all ETL tools solve the same problems, so it’s important to distinguish between batch processing, orchestration, and real-time stream processing.

1. Data Volume and Scale

For large-scale datasets or distributed processing, tools like PySpark are well suited due to their ability to process data across clusters. For smaller or local workloads, tools such as Polars, petl, or Bonobo provide fast and lightweight alternatives.

2. Workflow Complexity and Orchestration

If your ETL pipelines involve complex dependencies, scheduling, or retries, orchestration tools like Apache Airflow, Luigi, or Dagster are a better fit. These tools focus on managing execution order and reliability rather than performing the data transformations themselves.

3. Real-Time vs Batch Processing

For true real-time or event-driven processing, Bytewax or PySpark Structured Streaming are appropriate choices. These tools are designed to process continuous streams of data rather than periodic batch jobs.

For batch or incremental ingestion (for example, syncing APIs or databases on a schedule), dlt is a strong option.

4. Ease of Use and Maintainability

For teams prioritizing simplicity and readability, lightweight tools like petl or Bonobo can be easier to adopt and maintain. For production systems where observability and data lineage matter, Dagster provides stronger tooling and visibility.

5. Deployment and Infrastructure

Consider where and how pipelines will run. Tools like Airflow, Dagster, and PySpark integrate well with containerized and cloud-based environments, while simpler libraries are often better suited for scripts or smaller services.

How to Use Python for ETL

Python is one of the best programming languages for ETL processes. This is because of its simplicity, large number of libraries, and ability to handle complex data workflows. To perform ETL with Python, you can follow these core steps:

1. Extract Data:

• Python allows you to extract data from many sources. These include databases like MySQL and PostgreSQL, APIs, and file formats such as CSV, JSON, and Excel.
  
• You can use libraries like pandas for working with CSV and Excel files. For APIs, requests is commonly used. For database connections, sqlalchemy or psycopg2 are popular choices.

Example using pandas:

python
import pandas as pd
data = pd.read_csv('data.csv')

2. Transform Data:

• Data transformation includes cleaning, enriching, and reshaping the extracted data. Python libraries like pandas and NumPy make it easy to manipulate data. They allow users to filter, aggregate, and join data with simple commands.
  
• You can also apply functions, handle missing data, and modify data types.

Example transformation with pandas

python
data['new_column'] = data['existing_column'].apply(lambda x: x * 2)
transformed_data = data.dropna()  # Remove missing values

3. Load Data:

• Once the data is transformed, it needs to be loaded into the desired destination, such as databases, data lakes, or cloud storage.
• You can use libraries like sqlalchemy to load data into SQL databases or boto3 to send data to AWS S3.

Example loading data to a SQL database:

python
from sqlalchemy import create_engine
engine = create_engine('postgresql://user:password@localhost/dbname')
transformed_data.to_sql('table_name', engine, if_exists='replace')

Python Libraries to Streamline ETL:

• Pandas: For quick data extraction, transformation, and loading tasks involving structured data.
• SQLAlchemy: This is used to connect to databases and perform SQL operations.
• Airflow: For scheduling and orchestrating complex ETL workflows.
• PySpark: For processing large datasets with distributed computing.
• Bonobo: For modular and maintainable ETL pipelines.
• DuckDB: For fast local analytics and ETL-style transformations over files (CSV, Parquet) using SQL in Python.

Python has many libraries to help you build ETL pipelines. These pipelines can scale to fit your data needs. You can use Python for simple file transformations or complex workflows. It gives you the right tools to automate the process and make it easier.

Conclusion

Python offers a rich ecosystem of tools for building ETL pipelines, from lightweight libraries for simple transformations to distributed frameworks and orchestration systems for complex workflows. Tools like PySpark excel at large-scale processing, Airflow and Dagster help manage pipeline execution, and Bytewax enables real-time stream processing.

As data architectures evolve, many teams now combine traditional Python ETL tools with platforms designed for continuous or right-time data movement. Estuary, the Right-Time Data Platform, enables teams to move data when they choose — sub-second, near real-time, or batch — without managing custom streaming infrastructure. Used alongside Python-based ETL and orchestration tools, Estuary helps teams build dependable, scalable pipelines that support both operational and analytical use cases.

The best ETL approach depends on your data volume, latency requirements, and operational complexity. By combining the right Python tools with modern data movement platforms, teams can build flexible pipelines that adapt as data needs grow.

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