Here are five things you should know about top ETL Python frameworks:

  1. ETL is the leading data integration method for e-commerce companies.

  2. Python is one of the most popular programming languages for building ETL pipelines. 

  3. Python frameworks can help you create more successful ETL pipelines.

  4. Top ETL Python frameworks include Bonobo, Bubbles, pygrametl, and Mara.

  5. Integrate.io is a data warehouse integration platform that lets you build ETL Python frameworks from scratch. 

ETL (extract, transform, load) is the leading integration method for data-driven e-commerce companies the world over. By providing an efficient way of extracting information from different sources and collecting it in a centralized data warehouse, ETL is the engine that powers business intelligence activities like reporting and data analysis.

While ETL is a high-level concept, there are many ways of implementing ETL under the hood, including both pre-built ETL tools and coding your own ETL workflows. Thanks to its ease of use and popularity for data science applications, Python is one of the most widely used programming languages for building ETL pipelines.

Python frameworks streamline the ETL development process for e-commerce companies like yours, but what are the best ones to use? In this article, learn about the top ETL Python frameworks and how Integrate.io can help you build successful ETL pipelines, even if you lack data engineering or programming knowledge.

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Integrate.io is a new data warehouse integration platform for e-commerce. It helps companies build Python ETL workflows from scratch, even if they lack programming and data engineering skills. Integrate.io also streamlines ELT, ReverseETL, and Change Data Capture (CDC). Email hello@integrate.io to learn more about a 7-day Integrate.io demo. 

What is an ETL Python Framework?

An ETL Python framework is a foundation for developing ETL software written in the Python programming language.

In general, Python frameworks are reusable collections of packages and modules intended to standardize the application development process by providing a common functionality and development approach. For example, some of the most popular Python frameworks are Django for web application development and Caffe for deep learning.

ETL Python frameworks have been created to help e-commerce retailers perform batch processing on massive quantities of data. By using these frameworks, businesses can move data to a target management system like a data warehouse (Snowflake, Oracle, AWS Redshift, Microsoft Azure, etc.) and then run that data through business intelligence (BI) tools for real-time deep insights into their e-commerce operations. Doing this can provide the following e-commerce intelligence for retailers:

  • How customers interact with retailers across multiple channels and devices.

  • The most valuable customers who are interested in a company’s products and services. Sales and marketing teams can then target those customers and move them through their dataflows and funnels. 

  • How sales, marketing, and customer service teams perform and execute day-to-day tasks

The top ETL Python frameworks make it easier to define, schedule, and execute data pipelines using Python. You can extract data, transform data into the correct formats, and execute ETL jobs without the stress.

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Top ETL Python Frameworks vs. Libraries

The terms “framework” and “library” are often used interchangeably, even by experienced developers. Both frameworks and libraries are collections of code written by a third party with the goal of simplifying the software development process. However, there are important differences between frameworks and libraries that you should know about, especially when it comes to ETL Python code:

  • A software library is a collection of helper functions and objects to assist with the software development process. Libraries allow developers to write “plug and play” code, inserting library functions in their code base as needed to save the time and effort of writing these functions themselves.

  • A software framework may consist of one or more libraries, all oriented toward a common purpose. Unlike libraries, frameworks usually dictate the overarching structure and architecture of your application, defining a design philosophy that developers must obey. Frameworks are suited to a “fill in the blanks” style of application development, in which developers insert the necessary code in order to make the framework function.

4 Top ETL Python Frameworks

Here are four of the top Python ETL frameworks you should consider.

1. Bonobo

Bonobo bills itself as “a lightweight ETL framework for Python 3.5+.You can easily extract information from a variety of sources, including XML/HTML, CSV, JSON, Excel files, and SQL databases. Then, you can use pre-built or custom transformations to apply the appropriate changes before loading the data into your target data warehouse.

Data in Bonobo is streamed through nodes; each node runs in parallel whenever possible on an independent thread, slashing runtime and helping you avoid troublesome bottlenecks.

Bonobo’s developers prioritized simplicity and ease of use when building the framework, from the quick installation process to the user-friendly documentation. Bonobo also includes integrations with many popular and familiar programming tools, such as Django, Docker, and Jupyter notebooks, to make it easier to get up and running.

Bottom line: One of the top ETL Python frameworks, Bonobo appeals to many different situations thanks to its ease of use and many integrations.

2. Bubbles

Bubbles is “a Python framework for data processing and data quality measurement.” Instead of implementing the ETL pipeline with Python scripts, Bubbles executes ETL pipelines using metadata and directed acyclic graphs. Graph nodes represent each operation in the ETL pipeline (e.g. data aggregation, data filtering, data cleansing, etc.).

The core concept of the Bubbles framework is the data object, which is an abstract representation of a data set. Bubbles can extract information from sources, including CSV files, SQL databases, and APIs from websites such as Twitter.

Bottom line: Bubbles is best-suited for developers who aren’t necessarily wedded to Python, and who want a technology-agnostic ETL framework.

What happens if you don't have the time, resources, or skills to use these top ETL Python frameworks? Integrate.io can help! This e-commerce data warehouse integration platform lets you build your own Python ETL workflow from scratch. There's no complex code or complicated jargon. Email hello@integrate.io to learn more about a 7-day Integrate.io demo. 

3. Top ETL Python Frameworks: pygrametl

pygrametl describes itself as “a Python framework which offers commonly used functionality for development of Extract-Transform-Load (ETL) processes.” First made publicly available in 2009, pygrametl is now on version 2.6, released in December 2018. 

Pygrametl is compatible with both CPython (the original Python implementation written in the C programming language) and Jython (the Java implementation of Python that runs on the Java Virtual Machine). This makes it a good choice for ETL pipelines that may have code in multiple programming languages.

In general, pygrametl operates on rows of data, represented under the hood as Python dictionaries. The framework also includes support for basic parallelism when running ETL processes on multi-core systems.

Bottom line: pygrametl’s flexibility in terms of programming language makes it an intriguing choice for building ETL workflows, making it one of the top ETL Python frameworks.

4. Mara

Mara is “a lightweight ETL framework with a focus on transparency and complexity reduction.”  The framework has certain principles and expectations for its users, including:

  • The use of PostgreSQL as a data processing engine.

  • A web-based UI for inspecting, running, and debugging ETL pipelines. 

  • A priority queue that ranks nodes on the cost (i.e. time) of executing them, with costlier nodes running first.

To date, Mara is still lacking documentation, which could dissuade anyone looking for a Python ETL framework with an easier learning curve. However, Mara does provide an example project that can help users get started. 

Bottom line: Mara is an opinionated Python ETL framework that works best for developers who are willing to abide by its guiding principles. However, it does not run on Windows. 

How Integrate.io Helps You Use the Top ETL Python Frameworks

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If you’re looking to perform ETL in Python, there’s no shortage of ETL Python frameworks at your disposal. But as your ETL workflows grow more complex, hand-writing your own Python ETL code can quickly become complicated—even with an established ETL Python framework to help you out.

Although the top Python ETL frameworks above are a great help for many e-commerce companies, they're not the right fit for every situation. None of these frameworks cover every action you need to build a robust ETL pipeline that incorporates input/output, database connections, parallelism, job scheduling, configuration, logging, monitoring, and more. Even if you use one of these top Python ETL frameworks, you'll still need an expert-level knowledge of Python and ETL to successfully implement, test, deploy, and manage an ETL pipeline all by yourself. Alternatively, you’ll need to hire a team of data engineers, which can work out expensive

For these reasons, many businesses are turning to Integrate.io, which comes with more than 100 pre-built integrations between databases and data sources, dramatically simplifying the ETL development process. Integrate.io includes the Integrate.io Python wrapper, allowing you to access the Integrate.io REST API from within a Python program. You can rely on Integrate.io to do the ETL heavy lifting for you, and then build your own Python scripts to customize your e-commerce pipelines as necessary. With Integrate.io, you don’t have to worry about the complexities of ETL such as data structures, data transformation, dependencies, schemas, command lines, and big data.

It’s not just ETL. Integrate.io offers other data integration methods such as ELT, Reverse ETL, and CDC, providing your e-commerce business with more flexibility. You can move data between sources and target destinations with out-of-the-box data connectors and generate unparalleled insights into your e-commerce enterprise. 

Integrate.io provides you with an alternative to these top ETL Python frameworks. You can create your own framework based on the needs of your e-commerce operations and optimize data management and transformation. Email hello@integrate.io to learn more or schedule a demo now