Extract, transform, load (ETL) is the most critical component of data warehousing because it turbocharges data transfer between systems. The best ETL tools in 2021 single-handedly define data warehouse workflows. But which ETL tool should you choose? 

From JavaScript and Java to Apache Hadoop and Go, many ETL solutions fit your needs. But Python dominates the ETL space. It's a high-level and general-purpose programming language used by many of the world’s biggest brands.

There are well over a hundred Python tools in 2021 that act as frameworks, libraries, or software for ETL. You'll find the best six below. But remember, you’ll still need to know Python for all of these tools. (It takes 8 weeks, on average, to learn the basics of this programming language.)

Before you learn the best six Python ETL tools in 2021, there's a simpler alternative you need to hear about. Xplenty is a no-code cloud-based solution used by Fortune 500 companies to extract, transform, and load data for business intelligence and analytics. Because it requires no code, you don’t need to learn Python. We’ll tell you more about Xplenty later.

Table of Contents

  1. Why Use Python for ETL Pipelines in 2021?
  2. Apache Airflow
  3. Luigi
  4. pandas
  5. petl
  6. Bonobo
  7. Bubbles
  8. Other ETL Tools, Libraries & Frameworks
  9. The Better ETL Solution

Why Use Python for ETL Pipelines in 2021?

First up, let's talk about why you would set up ETL pipelines with Python in 2021. Why not use an ETL tool? Developers who live and breathe ETL create and use these tools, right?

Right. ETL tools prove useful for dealing with complex schemas and massive amounts of big data, making them, hands down, the better choice for data-driven organizations like yours. Sure, you can use something like SQLAlchemy to execute ETL in Python. But, this process is time-consuming, labor-intensive, and overwhelming.

There are only three situations where Python makes sense: 

  • You feel comfortable with Python and want to build an ETL tool.
  • You have extremely simple ETL requirements.
  • You have a unique, hyper-specific need you can only meet by custom coding an ETL solution through Python.

If you fit into one of these categories, you have lots of options.

The tools below are the top six Python tools available for ETL in 2021. We selected these Python tools based on usability, popularity, and diversity. As we mentioned before, you’ll still need to know Python to use these tools. However, the platforms below make it easier to execute ETL pipelines with this programming language. If you don’t know Python or don’t want to code pipelines from scratch, we recommend looking at Xplenty’s features 

Recommended Reading: Other ETL Tools, Libraries, and Frameworks

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1. Apache Airflow for Python-Based Workflows

Apache Airflow is an open-source Python-based workflow automation tool for setting up and maintaining powerful data pipelines. Airflow isn't an ETL tool per se. But it manages, structures, and organizes ETL pipelines using something called Directed Acyclic Graphs (DAGs). 

DAGs form relationships and dependencies without defining tasks, running a single branch multiple times and skipping branches from sequences when necessary. 

For example, you can make A run after B and make C run every 2 minutes. Or make A and B run every 2 minutes and make C run after B.

Typical Airflows look like this:

Metadata database > Scheduler > Executor > Workers 

And here's how it works:

  • The metadata database stores workflows/tasks (DAGs).
  • The scheduler (typically run as a service) uses DAG definitions to select tasks.
  • The executor determines which worker executes tasks. 

Workers are the processes that execute the logic of workflows/tasks.

Apache Airflow makes a fabulous addition to your ETL toolbox because it's so valuable for management and organization. Plus, Airflow has Google Cloud and AWS hooks and operators, making it incredibly useful for cloud warehousing environments. 

When Does Apache Airflow Make Sense?

If performing long ETL jobs or when ETL has multiple steps. (Airflow lets you restart from any point in the ETL process.) That said, Apache Airflow is not a library, so you have to deploy it, and this makes little sense for small ETL jobs.

Facts About Apache Airflow

  • Maxime Beauchemin from Airbnb started the Apache Airflow project. 
  • Airflow won InfoWorld's Best of Open Source Software Award in 2020.
  • The majority of Airflow users leverage Celery to simplify execution management.
  • You can schedule automated DAG workflows via the Airflow WebUI. 
  • Airflow uses a command-line interface, extremely useful for isolating execution tasks outside of scheduler workflows.
  • Prefect is a solid Airflow alternative with advanced features. You can migrate DAGs straight over.
  • Apache Airflow currently has an average user rating of 4.1/5 on G2.com, based on 24 reviews. Users describe it as "stable," "fantastic," but sometimes "frustrating." 

As you can see, executing Apache Airflow can still be complicated. Xplenty provides a no-code alternative to Python, automating complicated data pipelines. 

2. Luigi for Complex Python Pipelines

Luigi is an open-source Python-based tool that builds complex pipelines. Developed by Spotify to automate its insane workloads (think terabytes of data every day), Luigi serves data-driven corporations like Stripe and Red Hat.

There are three benefits to Luigi: 

  • Dependency management with stellar visualization.
  • Failure recovery via checkpoints.
  • Command-line interface integration.

The primary difference between Luigi and Airflow is the way these Python tools execute tasks and dependencies. On Luigi, you'll find "tasks" and "targets," and tasks consume targets. (A task will plop out a target; another task will eat that target and plop out another target.) This target-based approach is perfect for simple Python-based ETL, but Luigi struggles with hyper-complex tasks.

When Does Luigi Make Sense?  

When you need to automate simple ETL processes like logs. (Luigi handles logs quickly and with little setup.) Luigi's strict pipeline-like structure, however, limits complex tasks. Plus, even simple ETL processes require Python coding skills.

Facts About Luigi:

  • You can't interact with processes once Luigi runs tasks.
  • Unlike Airflow, Luigi doesn't automatically schedule, alert, monitor, or sync tasks to workers.
  • Luigi's UI (or lack thereof) is a pain point.
  • Towards Data Science says Luigi uses a "target-based approach," but its UI is "minimal," with no user interaction for running processes. Plus, it doesn't support distributed execution or triggering. 

Recommended Reading: Building an ETL Pipeline in Python

3. pandas for Data Structures and Analysis Tools

If you've been working with Python for a while, you might know about pandas, a library that provides data structures and analysis tools for Python. pandas adds R-style data frames that make ETL processes like data cleansing easier.

In an ETL context, pandas executes many tasks successfully if you're willing to put in the time. Plus, it's relatively easy to run: Set up simple scripts to load data from a Postgre table, transform and clean that data, and write the data to another Postgre table. 

But it's not all fun and games: pandas performs relatively poorly on in-memory and scalability. Sure, you can scale the tool using parallel chunks, but the process isn't as simple as using something like Airflow. 

When Does pandas Make Sense? 

When you need to extract data, clean it, transform it, and write it to Excel, a CSV file, or an SQL database. Once you work with large data sets, it makes sense to adopt a more scalable approach.

Facts About pandas

  • NumFocus sponsors pandas.
  • Many Python users choose pandas for ETL batch processing.
  • pandas launched in 2008.
  • pandas boasts a rating of 4.5/5 on G2.com. (That's higher than Airflow.) Users say it's "powerful," "very practical," but there is "a learning curve."

4. petl as a Python ETL Solution

For ETL, petl is probably the most straightforward solution. The tool builds tables in Python, extracts data from multiple sources, and facilitates all kinds of ETL tasks. It's like pandas in functionality, but it doesn't offer the same level of analysis. 

The petl tool handles hyper-complex datasets relatively well, makes good use of system memory, and has reliable scale. But, it lacks in the speed department. Still, it's much easier to leverage petl than building ETL using SQLAlchemy or other custom-coded solutions.

When Does petl Make Sense? 

If you need the basics of ETL without the analytics and when you're not bothered about speed.

Facts About petl

  • petl isn't known for its speed or ability to handle large datasets.
  • petl stands for "Python ETL."
  • Towards Data Science calls petl "basic" and "trivial" but commends it for supporting standard transformations like sorting, joining, aggregation, and row operation.

5. Bonobo as a Lightweight Python ETL Framework

Bonobo is a lightweight Python ETL tool that's relatively easy to use for rapidly deploying pipelines and executing them in parallel. Bonobo extracts data from multiple sources — CSV, JSON, XML, XLS, SQL, etc. — and follows atomic UNIX principles. The best thing about Bonobos is that users don't have to learn a new API. So if you know Python, working in Bonobo is a breeze. You can build graphs with business requirements in mind, create libraries, and perform (simple) ETL batch automation.

Bonobo is a decent choice for Python users. It's open-source, scalable, and handles semi-complex schemas. 

When Does Bonobo Make Sense? 

When completing simple, lightweight ETL jobs. And when you don't have the time to learn a new API. You’ll still need knowledge of Python, though, so no-code Xplenty might provide a better option

Facts About Bonobo

  • An official Bonobo Docker extension lets you run jobs within Docker containers.
  • Bonobo has CLI execution.
  • Bonobo has Graphviz for ETL job visualization.
  • There's an official SQLAlchemy extension (in alpha).
  • A reporter for Python Pandemonium found writing his first-ever ETL in Python with Bonobo a relatively simple process. 

6. Bubbles as a Python Framework for ETL

Bubbles is another Python framework that runs ETL. Unlike some other frameworks, Bubbles uses metadata to describe pipelines. Use it for data integration, data cleansing, data auditing, data monitoring, and more. Although written for Python, Bubbles' author says his tool isn't Python-exclusive.  

Another benefit of Bubbles is that it's "technologically agnostic," which means you don't have to worry about working with or accessing data — just the transformation.

When Does Bubbles Make Sense? 

When you need a rapid ETL setup that's technologically agnostic. And when you want the freedom to focus solely on ETL processes. 

Facts About Bubbles

  • Bubbles is abstract, with a focus on ETL rather than learning Python. Open Knowledge Labs says: "Why should someone who just wants to achieve his goal of extracting, transforming, and presenting the data care about the underlying technology and query language?"  

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Other ETL Tools, Libraries & Frameworks

There are simply too many Python (and other) tools that handle ETL to include in this post. We tried to keep our list simple by including popular ETL options that have different use cases. But there are plenty of other tools that we could mention. 

In this section, we're going to list other popular ETL platforms by language.

Python

  • BeautifulSoup: This Python tool pulls data out of webpages (XML, HTML). It integrates with tons of ETL tools like petl.
  • PyQuery: Also extracts data from webpages but with a jquery-like syntax.
  • Blaze: This is an interface that queries data. Part of the "Blaze Ecosystem," a framework for ETL that uses Blaze, Dask, Datashape, DyND, and Odo.
  • Dask: Use Dask for Parallel computing via task scheduling. It also processes continuous data streams. Also part of the "Blaze Ecosystem."
  • Datashape: A simple data-description language similar to NumPly. It describes in-situ structured data with no canonical transformation.
  • DyND: The Python exposure for DyND — a C++ library for dynamic, multidimensional arrays.
  • Odo: Move data between multiple containers. Odo uses the native CSV loading capabilities of SQL databases, making it faster than loading with Python.
  • Joblib: More tools that use Python functions for pipelines, but Joblib has a few unique perks that make it suitable for certain jobs such as NumPy arrays.
  • lxml: Processes HTML and XML in Python.
  • Retrying: Lets you add retry behavior to executions.
  • riko: A Yahoo! Pipes replacement that's useful for stream data. Not a full-blown ETL solution, but it's pure Python, and it makes extracting data streams much easier.

Cloud-Based

  • Xplenty: Do you want to set up automated pipelines across a massive variety of sources using best-of-breed visualization and rapid integrations? You need Xplenty! Point-and-click, 200+ out-of-the-box integrations, Salesforce-to-Salesforce integration, and more. No code required whatsoever. The only solution for ETL.
  • AWS Data Pipeline: Amazon's data pipeline solution. Set up pipelines between AWS instances and legacy servers.
  • AWS Glue: Amazon's fully managed ETL solution. Manage it directly through the AWS Management Console.
  • AWS Batch: Used for batch computing jobs on AWS resources. It has good scalability that suits engineers on large compute jobs.
  • Google Dataflow: Google's ETL solution for batch jobs and streams.
  • Azure Data Factory: Microsoft's ETL solution.

Miscellaneous

  • Toil: This USCS project handles ETL almost identically to Luigi. (Use Luigi to wrap Toil pipelines for additional check pointing.)
  • Pachyderm: Another alternative to tools like Airflow. Here's a great GitHub writeup about some of the simple differences between Airflow and Pachyderm. (Pro tip: Paychyderm has an open-source edition on its website.)
  • Mara: Yep, another ETL framework that uses Python's capabilities. It sits somewhere between pure Python and Apache Airflow, and it's fast and simple to set up.
  • Pinball: Pinterest's workflow manager with auto-retries, priorities, overrun policies, and horizontal scalability.
  • Azkaban: Created by LinkedIn, Azkaban is a Java-based tool for Hadoop batches. But if you're trying to run hyper-complex Hadoop batches, think about Oozie instead.
  • Dray.it: A Docker workflow engine that helps with resource management.
  • Spark: Set up your entire batch streaming ETL.

Other Languages

We won't dive too deep into the tools below. But here's a list of ETL platforms outside of the Python space.

Java

GO

JavaScript (Node.js)

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The six Python frameworks, libraries, and tools on this list prove useful for less-complex jobs. But most growing enterprises need a speedier, scalable solution that leverages multiple tool layers for comprehensive ETL.  

Xplenty is a no-code, cloud-based solution that builds robust pipelines in minutes, with 200+ integrations that make ETL a piece of cake. It requires no data engineering experience. 

Xplenty has limitless features. Most ETL tools move data from Salesforce to a warehouse, but Xplenty moves it back again. Then there's the super-simple workflow creation for defining dependencies between tasks. The easy data transformations. A reliable REST API. Enhanced data security and compliance. And free customer support for all users. It's the only ETL solution you'll ever need. 

Forget about Python-based tools in 2021. You need a no-code ETL solution that lets you extract, transform, and load data without having to start from scratch. Xplenty is pain-free but sturdy enough to handle even the most demanding of workloads. 

Xplenty is the future of ETL. No code. No messing around. It's data transformation on your terms. Just the way you like it. Click here to schedule an intro call with our support team to see if Xplenty is the right fit for you.