The 7 best Python ETL tools in 2023

1. Keboola 

Keboola is a data platform as a service that helps you automate all your data operations. 

Its core feature is to build and automate ETL, ELT, and reverse ETL data pipelines.

Keboola is a platform that lets you plug and play your favorite technologies. You can use Python for your data transformations, including pure Python or any Python library of your choice (Pandas, PySpark, NumPy, etc.).

Key features:

• Built for every user. Build data pipelines via a command-line interface, code, or no-code with visual drag-and-drop features. Kebola allows every user to self-serve their data analytic needs.
• Out-of-the-box extraction and loading. Keboola offers 250+ components that can connect data from multiple data sources to target data warehouses or data destinations with a single click. Saving you time coding, integrating new APIs, and scaling your operations to complex data pipelines. 
• Powerful and flexible Python transformations. You can write all your transformations in Python, choosing any library (PySpark, Pandas, …) and incorporating it into your data pipeline workflow. The transformations can easily be scheduled, triggered, and scaled with a couple of clicks. 
• End-to-end automation. Data flows can be automated with Orchestrators and Webhooks. Every job is fully monitored, so you can always keep an eye on execution. And every ETL data pipeline can easily be shared and reused to save you development time. 
• Extensive data use cases beyond ETL: enterprise data security for every business size, data governance and master data management, DataOps (development branches and versioning, CDC to speed up replication, CLI), Data Catalog for data sharing, and machine learning toolbox.

Best for: Teams of technical data experts (data scientists, data engineers, data analysts) and data-driven business experts who would like an all-in-one ETL solution.

“Keboola is a nice tool for data management. It is very intuitive even from the beginning of usage. It offers many custom components to import/export data and also the possibility to create your own. Data manipulation is also simple - with SQL, Python, or R.” Marketa P., BI-Analyst.

2. Apache Airflow

Apache Airflow is an open-source Python framework that lets you author, schedule, and monitor workflows. The workflow can be an ETL process or a different type of data pipeline.

Key features: 

• Build ETL jobs as DAGs (directed acyclic graphs), that chain multiple Python scripts into a dependency graph. This allows it to run processes in parallel, such as parallelizing extraction from multiple sources at the same time. Only after extraction is finished, do you trigger the Python transformation scripts.
• Easy to monitor ELT jobs through its interactive UI where you can visualize (and restart) workflow dependencies, failures, and successes.
• Extensible. With operators, you can extend the functionality of Airflow to cover other use cases as well as use the data tool as a data integration platform. 
• No versioning of data pipelines. There is no way to redeploy a deleted Task or DAG. What’s worse, Airflow doesn’t preserve metadata for deleted jobs, so debugging and data management are especially challenging.
• You’ll need some DevOps skills to get it running. For example, Airflow doesn’t run natively on Windows, you’ll have to deploy it via a Docker image.

Best for: a team of data engineers, who love the control over their ETL process by hand-coding the Python scripts.

3. Luigi

Originally developed by Spotify, Luigi is a Python framework that helps you stitch many tasks together, such as a Hive query with a Hadoop job in Java, followed by a Spark job in Scala that ends up dumping a table in a SQL database.

Key features: 

• Jobs are written in Python and Luigi’s architecture is highly intuitive.
• No distribution of execution, Luigi will overload worker nodes with big data jobs. This makes it more appropriate for small to mid-data jobs.
• Job processing is done via batch compute, so not useful for real-time workflows.

Best for: Backend developer automating simple ETL processes.

4. Pandas

The Pandas library simplifies data analytics and data transformations. Pandas’ core feature is the DataFrame object, a table-like data structure that allows you to manipulate data in a user-friendly way.

Key features: 

• Data transformations can be done extremely quickly and declaratively. From complex aggregations to data types coercion, Pandas relies on NumPy and C-like features that speed up complex transformations for a 
• Limited extraction and loading capabilities. Data is extracted from and loaded to file systems (Microsoft Excel, CSV, JSON, XML, HTML) and more rarely SQL databases, but the E and T functionalities do not scale well. 

Best for: Good choice for building a small data pipeline, for example for a solo business intelligence project or a proof of concept. The tabular design is also user friendly for beginners who can set up  a simple pipeline following an online tutorial.

5. petl

​​petl is a general-purpose Python package for extracting, transforming, and loading tables of data.

Key features: 

• Data processing is designed via lazy evaluations and iterators (aka, a pipeline will not be executed and load data until data is requested). petl will use minimal system memory and can scale to large data volumes but will perform poorly in terms of speed.
• Extendable - reuse petl’s boilerplate code to extend petl’s functionality to new data sources or data destinations. 
• Limited analytic transformations. petl’s main shortcoming is the simplicity of its transformation features. If you need more complex transformations for data analysis, rely on Pandas/PySpark. 

Best for: data engineering teams with simple ETL pipelines that don’t have speed constraints or complex transformations.

6. Bonobo

Bonobo is a lightweight Extract-Transform-Load (ETL) framework for Python users, giving tools for writing data pipelines using simple Python scripts. 

Key features: 

• The Bonobo framework atomizes every step of the ETL pipelines into Python objects and chains them together into a graph of nodes. The atomic design helps you limit the scope of each module and enhances testability and maintenance. 
• Parallelized data streams by design.
• Offers many prebuilt extractors and writers, but for the majority of data warehousing tasks, you’ll have to write your own.

Best for: data engineers and backend developers who are looking to organize their complex data pipelines with a Pythoneque tool, but are not looking at automating away the work behind code.

7. PySpark

PySpark is a Python API to access and use Apache Spark - the Scala programming language - directly via the familiar Python interface.

PySpark is designed to handle extremely large datasets with its parallel computing, lazy loading, and Resilient Distributed Datasets (RDDs).

Key features: 

• One of the best solutions for data scientists and machine learning engineers working on big data challenges.
• ETL tasks can be written in a SQL-like or Python-like form.
• Although PySpark offers fantastic transformation features, its extract and load capabilities are limited similarly to Pandas. With PySpark, you’ll get filesystems and SQL databases covered, but for more complex pipelines, you’ll have to write your own extractors and writers.

Best for: data scientists and machine learning engineers who want to process their own big data datasets.

How to pick the best Python ETL tool?

When choosing the right Python library for your data engineering tasks, pick one that:

1. Covers all the different data sources you need to extract raw data from.
2. Can handle complex pipelines used for cleaning and transforming data.
3. Covers all the data destinations (data lakes, data warehouses, SQL databases, filesystems), where you will load your data to.
4. Can scale easily if you have multiple jobs running in parallel to save time.
5. Are extensible - can be used as a tool not just for data engineering, but also by data scientists to create complex schemas for their data science initiatives.
6. Are easily monitored - observability is crucial for debugging and guaranteeing data quality.

If your focus is on the transformation layer and you need little Python code for extract and load tasks, pick the Pandas (simple data structures) or PySpark Python libraries.

If, on the other hand, you need to manage a more complex workflow, Keboola, and similar alternatives are your best guess for the Python ETL tool.

Choose Keboola for a scalable ETL process set up in minutes

Keboola is loved by engineers because of its simple-to-code Python ETL features that scale, are monitored by default, and are extensible with other tools.

Data experts pick Keboola because of its ease of use.

With its out-of-the-box components, you can set up ETL processes in minutes, with a couple of clicks.

Did we mention it’s free? 

Keboola offers an always free tier (no credit card required) so you can test and develop ETL data pipelines without breaking the piggy bank. 

Try Keboola for free

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