ETL (short for Extract, Transform, Load) is one of the most critical first steps needed for creating a successful data warehouse. Because the truth is, it’s not as simple as retrieving data from multiple sources, dumping it en masse, and calling it a day. Data needs to be prepped and normalized to make it easier for organizations to analyze them.
The ETL process is easy to understand in principle. Extraction retrieves the data from multiple sources and with often varying protocols. Next, it transforms the raw data to make it optimized and ready for your data systems. Finally, the data is loaded into the system in the most efficient way possible.
There are many tools and languages to do ETL, but Python is one of the top choices for a reason. Mainly because it’s easy to use, plus it has an extensive selection of third-party libraries and plugins specifically for ETL, so developers need not code everything from scratch.
This post will cover examples of ETL frameworks, which are the easiest and most comprehensive way to approach ETL in Python. It covers the entire process from extraction to loading and has all the tools and support systems you’ll ever need.
Bonobo is a relatively lightweight and easy-to-use ETL framework. It’s aimed at anyone with even the most basic Python knowledge, making the tool extremely accessible to data scientists and coders. The nature of the framework can be summed up with its tagline, “data processing for humans.”
How it Works
Part of the accessibility of Bonobo is in its methodology. Here, data is treated as a stream that goes through the ETL process (extracting, transforming, and loading) in a first-in, first-out (FIFO) order.
Because Bonobo is just a stream of data rows, the entire pipeline can be represented as a graph using the graphviz library, enabling developers to inspect and design pipelines visually.
Using this visual graph approach, developers can design complex data transformation processes relatively easily in Bonobo. Functions can be combined and chained together, then executed as a single Python file. And the flow doesn’t even have to be linear – they can diverge and converge at node points using conditional logic to process data as needed.
The biggest reason to use Bonobo is its ease of use. In fact, this ETL framework is aimed at smaller projects run by independent coders and individuals. Bonobo is nothing but Python code and should be easy to pick up for even Python newbies. At the same time, the minimalistic nature of Bonobo gives it a fast performance.
The visual graph approach is also a welcome feature that makes process visualization very easy. Debugging and editing is just a matter of removing or rearranging nodes. Plus, there are plenty of excellent resources and tutorials online to get anyone up and running.
All of these make Bonobo an excellent tool for beginners wanting to learn ETL methods.
But despite the simplicity, Bonobo can be a flexible and robust tool. It can extract data simultaneously from multiple sources and protocols, such as CSV, SQL, XML, JSON, and XLS, to name a few. Its functionality is also expandable with great plugins like Docker, SQLAlchemy, and Jupyter.
The limitation of Bonobo is that it can only handle smaller data sets, which is the original intent of the developers. Thus, it cannot tackle big data applications for larger organizations.
The way Bonobo handles data (as a series of rows in nodes) is also relatively simplistic, making it difficult to perform analysis work on the dataset as a whole. It’s the reason why Bonobo is a poor choice for advanced statistical analytics and data science operations.
Lastly, the development future of Bonobo is uncertain. It’s currently at the alpha stage, with version 1.0 yet to be released.
When to Use Bonobo
Due to its accessibility and limitations, Bonobo is ideal for small-scale projects or organizations that deal with smaller data sets. It’s also great as a beginner’s tool to learn the ropes or experiment with ETL processes.
However, for larger companies with mission-critical data warehouses, Bonobo is just too unreliable and limited at this point.
Like Bonobo, Mara is also a lightweight ETL framework for Python developers who want a simple way to create ETL functionality without having to code it from scratch. It’s aimed as an alternative to Apache Airflow but with a more straightforward approach.
How it Works
The main draw of Mara is reducing ETL complexity. It does this by using several assumptions, which, while greatly simplifying things, can tend to limit your options in certain situations.
One of these is that it’s only compatible with PostgreSQL as the database management system. Nor does it support in-app processing – all database operations must be done via command line tools. Every part of the ETL pipeline also needs to be written as declarative Python code in Mara.
But these assumptions aside, Mara is a pretty simple tool to use. The framework supports several UI options right out of the box, including a command line UI (based on pythondialog) and a web interface. You can use either UI to inspect and debug pipelines as they’re running quite quickly.
Despite the apparent limitations, Mara is relatively easy to use. It shares the same simplistic approach and even visual interface as Bonobo. In fact, it could even be argued that Mara offers quite a bit more visual control and information through its web interface, making debugging and development easier.
All of these make Mara development a rather fantastic experience.
Plus, unlike Bonobo, Mara can comfortably work with big datasets, which gives the platform better flexibility and performance in real-world projects.
The biggest negative for Mara is also what gives it its strength – the assumptions. For instance, if you don’t plan on using or switching to PostgreSQL, Mara is out of the window.
Mara is also currently not supported in Microsoft Windows. You’ll need to use solutions like Docker or the Linux Windows Subsystem to get around this limitation.
When to Use Mara
Mara is aimed at developers who want a more straightforward way to do ETL than what more complex libraries or frameworks offer.
Pygrametl can lay claim as one of the earliest ETL frameworks in Python still in use today. Released back in 2009, it has evolved into a mature, production-ready ETL tool often used in critical industries like finance and healthcare. While not as widespread as other tools, Pygrametl is nevertheless one of the more robust and powerful ETL frameworks available.
How it Works
The Pygrametl framework eschews the diagram or visual node approach of other tools and frameworks. Instead, it adopts an object-oriented method. Standard ETL functions like connecting to data sources and processing operations are abstracted and encapsulated in objects, which can then be imported into Python code.
Thus, creating ETL routines is as easy as instantiating the relevant Pygrametl objects in a Python code, then calling its function. Data objects allow developers to add in or remove data rows easily. Pygrametl also supports more complex operations like changing dimensions.
Because it works just like any Python API, it’s easy to integrate Pygrametl into existing Python code. It’s even compatible with the Jython, opening up the possibility of interfacing with JBDC drivers or existing Java programs within your ETL process.
For seasoned Python developers, this helps reduce the learning curve by eliminating the need to study another platform.
Pygrametl is also blazingly fast since it runs directly in the Python code. This is in comparison to other ETL solutions that rely on a GUI or visual diagram, which can slow things down.
Lastly, Pygrametl has been around for more than a decade, which means it’s relatively stable. There’s also a small but active community behind Pygrametl, and they offer excellent resources to help beginners get started with the framework.
Out of all the frameworks in this list, Pygrametl is arguably the least beginner-friendly. It supports no built-in visual design methodology, and creating successful ETL routines requires a good grasp of ETL concepts.
When to use Pygrametl
Pygrametl is a good choice for production-level data warehousing for larger organizations and companies. The long tenure and real-world use cases of Pygrametl are a testament to the reliability of the framework.
Pygrametl, however, is meant for more advanced users. For beginners or smaller teams with no extensive ETL experience, other frameworks like Bonobo or Mara with visual interfaces are a better fit.
There are many ways to approach ETL in Python. And even smaller companies with no data science experience can set up a data warehouse with these easy-to-use ETL frameworks.
But ETL, and data warehouses in general, is something crucial enough to most companies that they’d want to do it right. Sometimes, it’s better to get an expert company like Sphere Partners onboard to help give a solid foundation for your data warehousing efforts.
Are you interested in our data warehouse services? Contact us today and schedule a free consultation.