And now it’s time to get acquainted with the five and, in our opinion, the best Python ETL frameworks that will make life easier for you and your IT department.
If you don’t have decades of Python programming experience and don’t want to learn a new API to create scalable ETL pipelines, this FIFO-based framework is probably the best choice for you.
In particular, Bonobo provides advanced ETL tools for creating data pipelines capable of processing multiple data sources at the same time. Also, thanks to the SQLAlchemy extension, Bonobo allows you to connect the pipeline directly to SQL databases. Apart from SQL, this solution is also compatible with CSV, XML, JSON, XLS, etc.
This framework is one of the best in terms of ease of use. Here you will find an ETL process graph visualizer, which, together with the Graphviz library, makes process monitoring easier. Also, a detailed guide will come to the rescue, allowing you to start working with this method in 10-20 minutes. As for debugging processes, you just need to move or remove individual pipeline nodes through the GUI.
On the other hand, this simplicity makes Bonobo somewhat limited: as a rule, it is used by small independent teams to work with small data sets. In addition, the analysis of the entire data set is not available, which makes it impossible to use for statistical analysis.
Pygrametl is a Python framework that allows engineers to apply the most commonly used functions to develop ETL processes. This framework has been regularly updated since 2009.
Pygrametl allows users to create an entire ETL pipeline in Python but is also compatible with both CPython and Jython, so it can be a good choice if your project already has Java code and/or JDBC drivers.
Note that this product provides object-oriented abstractions for commonly used operations such as interacting between different data sources, running parallel data processing, or creating snowflake schemas.
The positive aspect of working with this framework is that there is an excellent manual for beginners, which helps even inexperienced Python developers to cope with it. On the other hand, with a not so large community of pygrametl fans, we can conclude that it is not intuitive enough for those who decide to apply approaches beyond the ones described in the manual.
In general, this Python framework for ETL pipeline will be a good option for production-level data warehousing for large-scale companies.
If you don’t want to code all the ETL logic manually, Mara might be a good choice for you. It is a lightweight, self-contained Python framework for the creation of ETL pipelines with a lot of out-of-box features. Many say it is the middle ground between simple scripts and Apache Airflow, which will be discussed below.
Mara has a well-designed web interface and CLI that can be inserted into any Flask application. Like other solutions from our top, Mara allows engineers to create pipelines for extracting and transferring data. Also, this Python framework for building ETL uses PostgreSQL as its data processing tool and takes advantage of Python’s multiprocessing package for pipeline execution.
In terms of benefits, Mara can handle large datasets (unlike many other Python frameworks for ETL). On the other hand, if you do not plan to work with PostgreSQL, this product will be useless to you. Please also note that currently, Mara is not compatible with Windows (as well as Docker).
We already mentioned this solution above, so note that this is not a standard ETL Python framework but a workflow management system (WMS) that allows you to plan, organize, and track any repetitive tasks, particularly ETL processes.
This is one of the most popular Python tools for orchestrating ETL pipelines. Despite the inability to process data independently, this product can be used to build workflows in the form of directed acyclic graphs (DAG). This approach ensures this solution with excellent scalability characteristics (in fact, this is why Apache Airflow is used by thousands of large companies around the world).
As for managing and editing graphs, it provides you with a convenient web interface for these tasks. If you are more comfortable working through the CLI, you will get a set of useful tools to help.
Why can’t Airflow be considered universal? First, because of the high cost of its implementation. Considering the difficulty for beginners (despite the very detailed and well-thought-out documentation), this product is still more aimed at integration in large companies. And finally, the Airflow functionality may be redundant for some teams, which means they will have to overpay for features they don’t need at all.
Luigi is another WMS that allows you to create long and complex pipelines for ETL processes. Like Airflow, Luigi is also designed to manage workflows by visualizing them as a DAG.
This product is easier to use than Airflow, but it has fewer features and more limitations (including the lack of a mechanism for scheduling tasks, difficulties in scaling, the inability to pre-start data pipelines, and the lack of task pre-validation). However, together they embody the best DataOps practices. From the perspective of end users, Luigi will provide them with an intuitive web interface through which they can visualize tasks and handle dependencies.
This solution is slower to develop than Airflow and has some gaps in the documentation. Still, the balance of the cost, ease of use, and features make Luigi a smart choice for those who are satisfied with the basic capabilities of running pipelines.