Python for ETL: Frameworks, Methods, Use Cases and Tools

The Basics of Python for ETL

ETL tools are used to extract, transform, and load data from various sources. Python ETL tools are a set of ETL tools and libraries built with the Python programming language.
The ETL process is a vital component in data warehousing and business intelligence. Python ETL tools offer a robust and flexible framework for data processing and integration. They usually have an extensive library of pre-built components and a supportive community.
Data engineers prefer Python ETL tools over others for their simplicity and versatility. After all, Python is an easy-to-learn programming language with a simple syntax structure.
Python ETL tools are suitable for small data processing tasks to large-scale data warehousing projects. The ability to integrate with other platforms is another advantage of Python ETL tools.

Types of Python ETL Tools

Script-based ETL tools: These tools use Python scripts to define the ETL process. Users can write custom scripts to integrate with specific data sources and manipulate data according to their needs.
GUI-based ETL tools: These tools provide a graphical user interface that enables users to design and execute ETL workflows visually. This approach is more intuitive and user-friendly for those who don't have a programming background.
Library-based ETL tools: These tools are built as Python libraries that can be integrated into other Python projects. Users can customize the ETL process by leveraging the library's functions and methods.

What Can Python ETL Tools Do?

Python ETL tools are capable of performing a wide range of data processing tasks, including:

Data extraction from various sources
Data cleansing and transformation
Data enrichment
Data loading into target systems

They can also integrate with other data tools such as databases, data warehouses, and cloud storage platforms. This flexibility makes Python ETL tools ideal for use in data integration and migration projects.

Understanding Python ETL Frameworks

ETL frameworks in Python build and maintain data pipelines. They have pre-built components for ETL pipelines.
They support various data sources, such as SQL databases, JSON, and XML files. Their primary purpose is to populate data warehouses with transformed and cleaned data. Python ETL frameworks enable complex DAG-based data pipelines to handle large volumes of data.
ETL frameworks in Python offer a flexible and scalable approach to data processing. Python ETL frameworks streamline data integration processes, reduce costs, and improve accuracy. They automate data processing tasks and allow organizations to focus on business objectives.
Python ETL frameworks integrate data from diverse and complex sources to gain insights. They simplify the ETL process for non-technical users. Python ETL frameworks offer a vast library of pre-built components for customization. They can handle structured, semi-structured, and unstructured data.
Python ETL frameworks also provide a cost-effective way to build data pipelines. They integrate well with other data tools and platforms. They enable organizations to make data-driven decisions with greater speed and accuracy.

Python for ETL: Top Use Cases

Data engineers can use Python for ETL in several ways.

Python ETL tools are essential for managing ETL jobs in data science and machine learning. They support real-time data sources and can handle both simple and complex transformations.
Python programming language provides powerful libraries for working with data. Some examples are pandas and NumPy. They can be used for data manipulation, cleaning, and transformation.
Python can be used to automate ETL processes using tools such as Airflow. They provide a platform for building, scheduling, and monitoring ETL workflows.
Python can be used to extract data from various sources such as databases, APIs, and file systems. Then you can load the data into a data warehouse or other storage systems.
Python can be used to transform data into a format that is suitable for analysis and reporting. Some examples are converting data types, aggregating data, and joining multiple datasets.
Python can be used to integrate data from multiple sources and systems, providing a unified view of data for reporting and data analytics.
Data transformations are complex when it involves multiple data sources and a variety of data structures. Python ETL tools can handle such complex data transformations.

Python's ease of use and readability make it accessible to data engineers of different skill levels.

10 Python ETL Tools for Data Engineers

Apache Airflow
Bonobo
Portable
Glue ETL
Pandas
Luigi
Petl
Pyspark
Odo
Riko

There are many Python ETL tools available, including those that integrate with Java and support machine learning. Check out these popular Python ETL tools, each with its unique features and capabilities.

1) Apache Airflow

Apache Airflow is one of the most powerful python-based ETL tools. It has a modular architecture that utilizes a message queue. This message queue can control and manage a potentially infinite number of workers. This infinite scalability makes it a popular choice for managing complex workflows.

Airflow allows users to write code that can generate dynamic pipelines instantaneously.
Airflow is also highly extensible. In Airflow, an operator is a single task that performs a specific action, such as extracting data from a source. With Airflow, users can create their own operators by defining Python functions or classes that perform custom tasks.
One of Airflow's core features is its elegant and lean pipeline design. This includes built-in parametrization with the powerful Jinja templating engine. This makes it easy to build flexible workflows using standard Python features.
As an example, Airflow allows scheduling tasks using various date and time formats. It can also support dynamic task generation through loops.
A good ETL tool should offer the ability to monitor, schedule, and manage workflows. Airflow includes a modern and robust web application for that. This makes it easy for users to monitor the status and logs of completed and ongoing tasks.
Another key benefit of Airflow is its robust integrations with many plug-and-play operators. You can use them for executing tasks on popular cloud platforms such as GCP. This makes it easy to apply Airflow to existing infrastructure and extend it to next-gen technologies.
Finally, Airflow is open-source and has a large and active community of users. These users are willing to share their experiences and contribute to its development. This makes it easy to learn and use, and anyone with Python knowledge can deploy a workflow using Airflow.

2) Bonobo

Bonobo is a lightweight ETL framework that's built for Python 3.5+. It provides a straightforward way to create data transformation pipelines using basic Python data types. These pipelines can be executed in parallel. Bonobo aims to be an all-purpose tool for common data tasks, without requiring the user to learn new APIs.

Bonobo includes pre-made tools for extracting data from and writing data to commonly used file formats. Some examples are CSV, JSON, XML, XLS, and more.
Bonobo also has official add-ons for SQL integration, and users can create their own extractors as well.
Bonobo simplifies the ETL process by using a functional programming approach. Bonobo also offers a library of pre-made transformation classes. You can use them to perform common data transformations, such as filtering, grouping, and aggregation.
Bonobo is simple to use, and users can get up and running in just 10 minutes if they already have Python knowledge. Its transformations are atomic, with each transformation having a specific, unique, small purpose. This enhances testability and ease of maintenance.
One potential downside of Bonobo is that it is still a relatively new ETL tool. As a result, it may not have the same level of community support as some other Python ETL frameworks.
Additionally, The lack of GUI may make it less appealing for users who prefer a visual approach to ETL development. Finally, Bonobo does not have native support for big data technologies like Hadoop or Spark. This may limit its scalability for larger data sets.

3) Portable

Portable is a no code ETL tool that provides over 300 ETL connectors for hard-to-find data sources.

It's an ideal solution for teams working with long-tail data sources. Moreover, the turnaround times for custom connectors can be as fast as a few hours. The pricing model for ETL connectors is fixed and predictable, with no data volume caps.
Portable is focused on data replication and follows the ELT architecture for data processing. The tool's specialty is extracting and loading data.
Portable supports the most common data warehouses, including Snowflake, Amazon Redshift, Google BigQuery, and PostgreSQL.
The roadmap for destinations includes MySQL, Clickhouse, Microsoft SQL Server, Microsoft Azure Synapse, and Databricks.
Portable helps engineers seamlessly extract data from APIs into ready-to-query schemas in data warehouses. The tool provides hands-on support, automated error handling, and notification functionality. However, it does not include data transformation.
Portable's pre-built data connectors focus on niche tools. Some examples are e-commerce platforms, subscription billing platforms, and marketing tools.
Portable's hands-on technical support is also a major benefit, as the team is on-call to provide turnkey solutions when issues arise.
Portable is ideal for analytics teams that need to integrate data from specific, long-tail applications that aren't supported by Fivetran.

4) Glue ETL

Glue ETL is a serverless data integration service. It simplifies data preparation for analysis tools. It can run Python-based ETL scripts on big data and is fully managed by AWS. This makes it a popular choice for data engineers.

AWS Glue ETL offers a centralized data catalog for managing data and metadata across multiple sources and formats. This service supports over 70 data sources, including databases, S3, and Redshift.
AWS Glue ETL provides a visual interface for building and managing ETL pipelines. This can save time and reduce the need for coding.
Glue ETL is beginner-friendly because it consolidates data integration needs into one service. It also eliminates the need for infrastructure management.
Glue ETL is suitable for interactively exploring and processing data. Data engineers can use their preferred IDE or notebook with data. AWS Glue supports different processing frameworks and workloads for greater flexibility.
GlueETL also allows you to use Python code and frameworks, like Spotify's Luigi or Apache Airflow, to create custom ETL workflows. Its CLI and support for Python code make it an attractive option for creating custom ETL workflows.
Finally, Glue ETL offers automatic schema discovery and mapping. This can simplify data preparation and reduce errors. Moreover, this service offers job monitoring and debugging tools. This helps to identify and resolve issues during ETL processing.

5) Pandas

Pandas is a popular open-source library for Python that provides data structures and analysis tools.

It is ideal for ETL tasks and is particularly useful for small to medium-sized datasets.
With Pandas, data scientists can easily extract, transform, and load data from various sources.
Pandas add R-style data frames, making data cleansing and transformation easier.
It is also a general-purpose library and can be used for a wide range of data analysis tasks.
However, it may not be the best choice for large-scale data processing and in-memory operations.
Pandas has an interactive HTML interface that makes it easy to manipulate and analyze data.
It can also be used in Docker containers for easy deployment and portability.
Pandas is a command line tool, but it can also be used in Jupyter Notebooks and other interactive environments.
Many Python users choose Pandas for ETL batch processing, and it boasts a high rating on G2.com.
Overall, Pandas is a versatile and powerful ETL solution. It is widely used by data scientists and other professionals working with data streams.

6) Luigi

Spotify created Luigi in 2012. It's an open-source Python ETL tool that helps build complex pipelines of batch jobs.

Luigi handles dependency resolution, workflow management, and visualization. It comes with Hadoop support built-in. Luigi is similar to GNU Make in concept, where tasks may have dependencies on other tasks.
One major difference is that Luigi is not just built specifically for Hadoop. You can easily extend it to other kinds of tasks.
With Luigi, you can automate the management of workflows, dependencies, and failures of ETL pipelines. This helps to simplify building complex and long-running ETL pipelines. You can use Luigi to build long-running pipelines
Luigi's infrastructure has diverse applications such as recommendations, top lists, A/B test analysis, external reports, and internal dashboards. It powers a wide range of data-related tasks with ease.
However, Luigi's tight coupling with cron jobs and the limitation on the number of worker processes can be scalability issues. Additionally, everything in Luigi is in Python. Therefore, you need good knowledge of Python to work with it.
Overall, Luigi is an excellent choice for data engineers and data scientists who need to build complex ETL pipelines. It is a specific tool designed to solve the problem of workflow management and dependency resolution, and it does it very well. Luigi is widely used by many enterprises and is an active open-source project with a large community of contributors.

7) Petl

Petl is a popular Python library for ETL. It offers an easy-to-use API for manipulating tabular data in the form of lists, dictionaries, and DataFrames. It is focused on ETL, making it more efficient than pandas when working with databases like MySQL or sqlite3.

Petl offers a wide range of functions for transforming tables with minimum lines of code. Petl does not load the database into memory each time it executes a line of code. Therefore, It is more memory efficient than pandas. Petl is considered lightweight and efficient. This makes it ideal for migrating between SQL databases.
However, Petl lacks functions for data analysis and visualization. It's one of the reasons why it is not as popular as pandas. Additionally, the library is still under active development and lacks proper documentation. This makes it less popular among the data scientists' community.
Before using Petl, it is important to keep in mind its limitations and make sure that it is the right tool for the job. Petl is best suited for ETL tasks that require efficient memory usage, working with databases, and manipulating tabular data in the form of lists and dictionaries.

8) Pyspark

Pyspark is a Python ETL tool designed to handle large-scale data processing and analysis.

Key features include:

Distributed processing
In-memory computation
Fault tolerance

These features make it a popular ETL tool for big data processing.

The unique goal of Pyspark is to provide a simple ETL interface for users. This interface comes with the added benefit of advanced algorithms for processing and analysis.

PySpark can automate the process of data transformation, filtering, and aggregation. This helps to reduce the complexity and time required for ETL tasks.

The pros of using Pyspark include:

High-speed processing
Ability to handle large volumes of data
Flexibility in working with different data formats and schema

Pyspark also offers a range of machine learning and data analysis algorithms for deeper insights.

On the other hand, the cons of Pyspark include its steep learning curve and complex installation process. Both of these can be a challenge for beginners.

To use Pyspark, users need to install the package and set up the environment. They can then load data from various sources, apply transformations, and store the results in a target system. It is important to have a good understanding of data manipulation, data schemas, and distributed computing before using the tool.

9) Odo

Odo is a Python library for data migration and ETL operations. It simplifies the process of moving data between different formats and systems. Odo provides a unified API that abstracts away all the complexity associated with transforming data from one format to another.

One unique feature of Odo is that it has only one function with one goal. That's to migrate data between different containers. It connects different data types via a network of conversions (hodos). In case of conversion failure, Odo has backup paths to complete the process.

Odo is suitable for working with huge datasets that need high-performance loading and migrating data between different containers or formats. It is particularly useful when working with SQL-based databases and CSV files.

The key features of Odo include:

High-performance data loading for huge datasets,
Support for large, out-of-core containers as well as small in-memory ones
Supports using CSV loading capabilities of SQL-based databases. This enhances the speed and efficiency of data loading.

However, it has some cons, such as not being frequently updated, which may impact its stability.

To use Odo, users must first install the package and set up the environment. Once this is done, they can load data from various sources using the unified API.

Data can be transformed as needed, using the network of conversions to connect different data types. Odo also supports data outside of Python, including CSV/JSON/HDF5 files, data on remote machines, and the Hadoop File System.

The results can then be stored in the target system, making the process of data migration and ETL much simpler and more efficient.

10) Riko

Riko is a Python framework designed for working with streams of data. It provides an easy-to-use API to transform, filter, and aggregate streaming data from different sources. These sources can be CSV files, web APIs, and databases.

One unique feature of Riko is its automation capabilities. This allows for the automatic execution of stream processors. Riko's CLI support makes it easy to create automated processes that can be run in the background.
Riko uses minimal computing resources to operate, allowing it to perform efficiently and without overloading the system it runs on. Riko supports native RSS/Atom feeds. This means Riko can work with RSS and Atom feeds without requiring additional software or libraries.
Riko is an ideal replacement for Yahoo Pipes and a good choice for startups with low technological expertise. Riko can handle multiple data streams simultaneously, with support for both synchronous and asynchronous APIs.
To summarize, with Riko, users can load data from various sources using the easy-to-use API. The data can then be transformed, filtered, and aggregated as needed. Riko's synchronous and asynchronous APIs allow teams to conduct operations in parallel execution.