How to Build an ETL Pipeline in Python: Step-by-Step Guide

Python is one of the most popular modern programming languages. Its easy syntax, extensive library ecosystem, and huge adaptability make it a go-to choice for data engineers worldwide, especially in data management. Most professionals use Python to do most of the data management tasks. One classic example of that is ETL. An ETL is a workflow that aims to take difficult-to-use or unstructured data and serve it to the source in a clean and structured format. Using Python for ETL can save time by running extraction, transformation, and loading phases in parallel. Python libraries simplify access to data sources and APIs, making the process more efficient. Additionally, these libraries come with built-in functions that facilitate data extraction, transformation, and analysis without the need for extensive custom coding.

In this comprehensive article, you’ll explore the entire process of setting up an ETL pipeline using Python.

What is ETL?

To understand Python ETL, let’s first explore ETL. ETL or Extract, Transform, Load is a method of integrating data between multiple systems. Originating from on-premise server solutions, ETL has evolved but retains its core principles. It synchronizes various data sources to centralized destinations like data warehouses, enhancing business intelligence.

An ETL pipeline involves:

Extraction: Extract data from multiple data sources such as APIs, flat files, or databases. This step processes raw data from disparate sources.

Transformation: Transform raw data into structured data through data processing tasks like cleansing, aggregation, and validation. This ensures data integrity and consistency.

Loading: Load transformed data into target systems like data warehouses. This step is crucial for maintaining data quality and handling various data formats.

Python ETL involves performing the entire ETL process using Python. Integration with cloud services enhances scalability, allowing efficient handling of large datasets and leveraging Big Data technologies.

Benefits of Using Python for ETL Process

Let's dive into the advantages of using the Python ETL framework:

1. Vast Ecosystem of Libraries and Frameworks

Python’s expansive ecosystem includes a wide array of libraries and frameworks specifically suited for ETL processes. These libraries make performing complex transformations on large datasets easier and more efficient.

For example, pandas and NumPy provide robust data manipulation capabilities, while SQLAlchemy enables seamless connectivity with various databases. On the other hand, Beautiful Soup is a great choice for web crawling and data extraction. Some libraries may require more code, but they provide enhanced value by maintaining a commit history of modifications throughout the pipeline steps.

2. Integration Capabilities

Python is popular for its seamless integration capabilities with various data sources and destinations, including diverse sources. It supports a wide range of databases, file formats, and APIs, allowing for smooth data extraction from different sources. Handling different data formats is crucial when integrating with various systems, as it ensures compatibility and efficient data processing.

Python simplifies handling data from various sources, such as SQL databases and APIs, within ETL pipelines. This streamlined approach to managing diverse data types is essential for effective data processing.

Additionally, Python is compatible with big data tools like Apache Spark (PySpark), RESTful APIs (requests), and messaging systems such as RabbitMQ (pika). This highlights its versatility in integrating with various systems.

3. ETL Pipeline Flexibility

Python supports multiple paradigms and programming styles, such as object-oriented, functional, and procedural. This flexibility creates customized ETL pipelines that can handle various data formats, enabling you to choose the best approach for designing and implementing your ETL logic.

For example, if your destination system involves cloud storage services like Amazon S3, boto3 is a powerful library that helps to load your data seamlessly.

4. Active Community of Data Scientists

Python has a large, supportive community of data scientists who actively contribute to the development and maintenance of libraries. The integration of data science into ETL processes is crucial for data engineers and businesses to derive valuable insights from data. Extensive documentation, tutorials, and forums are available to provide support and assistance. This empowers you to optimize your ETL workflows and stay up-to-date with the latest developments in data processing. Data professionals play a crucial role in utilizing Python’s libraries and tools to transform raw data into actionable insights.

How to Build an ETL Pipeline with Python in 9 Steps:

Building an ETL pipeline using Python is an effective way to manage data processing tasks efficiently. Here are the key steps to consider:

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Step 1: Define the Data Sources and Destinations

 Identify the sources you need to extract data from, such as SQL databases, flat files, or APIs. Handling data files, especially large datasets like CSV files, is crucial for efficient data management. Also, determine the target system where the transformed data will be loaded, such as data warehouses, databases, or data lakes.

Step 2: Plan the Flow of Data from Source to Destination

Map out the entire data flow, including the sequence of transformations required to convert the raw data into the desired format for the destination. This step helps you understand the dependencies and design the pipeline effectively.

Step 3: Set Up the Development Environment

To build the ETL pipeline, you'll need to set up a Python development environment. This typically involves installing Python and any necessary libraries or frameworks based on your requirements. Popular libraries for ETL tasks include pandas, NumPy, and SQLAlchemy.

Step 4: Extract Data from Sources

To extract data from various sources, Python offers a range of powerful libraries tailored to different data retrieval needs. For databases, SQLAlchemy can be used to establish connections, execute queries, and retrieve data efficiently. When working with APIs, you can use the requests library, as it simplifies the process of making API requests and handling responses.

Step 5: Transform the Data

Apply any necessary transformations to the extracted data. This might include cleaning the data, filtering rows, joining tables, or performing calculations. Handling null values is crucial when working with indexed data structures and dataframes, as it improves data quality. Data cleaning ensures that only valid, accurate, and properly structured data is loaded into the destination data store. Utilize libraries like pandas to manipulate the data effectively.

Step 6: Load the Data into Destinations

Once the data is cleansed and transformed, load it into the specified destinations, such as a database or data warehouse. Python offers a variety of robust libraries and interfaces that make this process efficient and seamless. For instance, with dlt (data load tool), an open-source library, you can easily load data into your destination system. Transformations occur within the target data store, simplifying the architectural design and enhancing scalability.

Step 7: Schedule the ETL Pipeline

To ensure the ETL pipeline runs regularly, you'll need to set up a scheduling mechanism. You can leverage Python modules like crontab or platforms like Apache Airflow for more advanced workflow management.

Step 8: Implement Error Handling and Logging

Handle exceptions that may occur during the ETL process. Ensure that errors are logged and handled properly to prevent data loss and ensure pipeline reliability. You can use Python's 'logging' module to keep a detailed log of pipeline execution.

Step 9: Deploy and Monitor the ETL Pipeline

Deploy your ETL pipeline to the production environment and set up monitoring to ensure its smooth operation. Monitor data quality, performance, and any potential failures.

How to Set up Python ETL? 

In this section, you will create a basic Python ETL framework for a data pipeline. The data pipeline will have essential elements to give you an idea about extracting, transforming, and loading the data from the data source to the destination of your choice. For the sake of simplicity, the data source will be CSV, and the destination will be MongoDB for a practical example. Here’s a detailed guide: 

Prerequisites

• Python. 
• A Windows PowerShell. 
• A working MongoDB database.

Step 1: Install & Import Required Packages

Importing the right libraries is the first step toward creating anything using Python. This step includes using two libraries to make an ETL pipeline: pandas and pymongo. To achieve this task, type the following code in the command line:

pip install pandas pymongo

Using a PowerShell script can also simplify the process of installing these packages, ensuring all dependencies are correctly managed.

The pandas library can be used to transform and manipulate data, and the pymongo library helps interact with MongoDB in a Python project. 

Step 2: Extracting Data from Source

The extraction process in Python varies based on the data source, including handling CSV files. A data source could include a database, flat file, CSV file, API, or an application. As mentioned above, this method involves extracting data from the CSV file in this ETL pipeline. Make a file named etl.py in your local machine and paste the code below to perform this task:

Import pandas as pd 
from pymongo import MongoClient

data = pd.read_csv(‘your_csv_file.csv’)

In the above code, the pandas library is imported, and the data from your_csv_file.csv is saved into the data variable. List comprehensions are a powerful feature in Python for efficiently filtering data, making the code more concise and Pythonic. Additionally, joining data is a specific operation during the transformation phase, essential for preparing the data before it is loaded into the destination data store.

Step 3: Transforming Data in Required Format

In this step, you will transform data in format and sequence according to your requirements. Data structures, particularly Directed Acyclic Graphs (DAGs) in Apache Airflow, are defined to organize tasks within scripts, ensuring proper execution and monitoring of data pipelines. With Python’s modern syntax and pandas’ data transformation functionalities like aggregation and manipulation, there is a lot of scope for enhancing datasets. Below are some examples of the basic transformations with CSV data using Python:

Sort and Filter

One common use case when transforming is ordering data. To do this, you can use methods like sort_values and filter from the pandas library. Here’s how to use these methods:

#Sort by filter
sorted = data.sort_values(by=[‘name’])

#Filter columns
just_filters = data.filter([‘name’, ‘is_student’, ‘target’])

In the above code, a hypothetical example is given as a name in the sort_values method to sort the name field alphabetically from the data stored in the CSV file. However, in the filter method, you can provide specific columns in this example (name, is_student, target), and only those columns will appear in the results.

Removing Duplicates

A common challenge in raw datasets is duplicate rows of data. The code below demonstrates finding and removing duplicates using the drop_duplicate method of the pandas library:

#Remove duplicates
remove_dups = data.drop_duplicates()

Above are the two common examples of basic transformations of Python ETL. You can choose to perform more transformation practices according to your requirements.

Step 4: Loading Data in MongoDB

This is the most technical step of the ETL process. However, if you have a basic working knowledge of Python, you’ll be fine. Here’s how to load the CSV data to MongoDB with Python:

It's crucial to produce clean and organized code output to enhance readability and usability, especially when dealing with multiple files and complex data imports.

First, you must connect MongoDB to Python:

mongoClient = MongoClient(‘MongoDB Atlas URL with port’) 
db = mongoClient['your_database'] 
collection = db['your_collection']

In the above code, replace your_database with the name of your MongoDB database and your_collection with the collection name.

To copy the CSV data to MongoDB, you must use the to_dict method of pandas and create JSON data (list of dictionaries) for inserting multiple records in MongoDB. Here’s the code:

jsondata = data.to_dict(orient='records')
collection.insert_many(jsondata)

Step 5: Complete Code for Python ETL 

Here is the complete code for the Python ETL pipeline you created. You can use Python IDE’s like Jupyter Notebook to execute this command:

Import pandas as pd 
from pymongo import MongoClient

data = pd.read_csv(‘your_csv_file.csv’)

mongoClient = MongoClient(‘MongoDB Atlas URL with port’) 
db = mongoClient['your_database'] 
collection = db['your_collection']

jsondata = data.to_dict(orient='records')
collection.insert_many(jsondata)

Note: The above code does not involve transformation to the CSV file before loading, as it depends on your specific requirements.

That's it. If you carefully follow the steps mentioned above, you can create a basic Python ETL framework to migrate data from the CSV file to MongoDB. 

6 Common Python Libraries Used in ETL Pipelines

Here is an overview of popular Python libraries in ETL pipeline development:

1. PyAirbyte

PyAirbyte is a robust Python library developed by Airbyte that allows you to easily build ETL pipelines. It facilitates automated data extraction and loading processes through Airbyte supported connectors, ensuring that data flows smoothly from source to destination. This helps maintain data integrity and consistency across the ETL pipeline.

2. Pandas

Pandas is a widely used Python library for data manipulation and analysis. You can handle missing data, remove duplicates, handle outliers, convert data types, and perform various transformations on the data. This helps to ensure that the data is in a suitable format for analysis. Ensuring data quality through data validation is crucial to confirm that the extracted data meets expected values within specific domains. Additionally, dimensionality reduction is a key technique offered by the Scikit-learn library for machine learning, which can be essential for effective data preparation and modeling.

3. SQLAlchemy

SQLAlchemy is a comprehensive Python SQL toolkit and Object Relational Mapper (ORM) that facilitates smooth interaction with databases. It allows you to map Python objects to database tables, simplifying the interaction with relational databases. 

You can define classes that represent database tables, and SQLAlchemy handles the mapping between the objects. This enables you to perform database operations using Python code rather than writing raw SQL queries.

4. requests

requests is a widely used Python library for making HTTP requests, which is crucial for collecting data from web services and RESTful APIs. The requests library also helps perform ETL tasks by enabling seamless data extraction and integration. It enables you to effortlessly send HTTP/1.1 requests without the hassle of manually appending query strings to your URLs. This streamlined approach facilitates easier integration with web services, making data extraction more efficient.

5. NumPy

NumPy is a fundamental library for numerical computing in Python, offering support for large multi-dimensional arrays and matrices. It plays a crucial role in preparing data for machine learning models by enabling efficient data manipulation and transformation. It is commonly used in ETL processes to efficiently perform complex data manipulations and transformations on large datasets.

6. Beautiful Soup

Beautiful Soup is a Python library designed for web scraping projects. It lets you parse and extract useful information from HTML and XML documents. Beautiful Soup plays a crucial role in preparing data for data visualization by cleaning and structuring the data for tools like Seaborn and Matplotlib. This enables you to search for specific elements or patterns within the HTML document using various search methods. You can use various methods, such as find_all(), find(), select(), and more, to filter the data based on specific criteria.

Best Python ETL Tools and Frameworks

Choosing the right ETL tool is crucial for building an efficient data pipeline. Python offers a range of powerful ETL tools suited for different use cases, from lightweight data transformations to large-scale data processing. Different ETL vendors offer tools that automate these processes and integrate multiple data sources. Additionally, a specialized engine handles the transformation work, allowing for complex operations like filtering and aggregating to occur efficiently during the data processing stages.

Meeting target system requirements is essential in ETL processes to ensure that data is modified to meet the specific needs of the target system before being loaded into a central location like a data warehouse.

Here are some of the top Python ETL tools:

• PyAirbyte – A modern, open-source ETL tool for easy data integration.
• Pandas – Ideal for small-scale ETL and data transformation.
• Apache Airflow – Best for workflow scheduling and automation.
• Luigi – Great for managing ETL dependencies in complex workflows.
• Bonobo – A lightweight framework for quick ETL jobs.
• PySpark – Designed for large-scale distributed data processing.

For a detailed comparison, check out our guide on Python ETL tools.

How can PyAirbyte Solve the Problems of Traditional ETL with Python?

Here's how PyAirbyte can efficiently address the challenges of traditional ETL using Python:

Complexity of ETL Scripts

Problem: Traditional ETL processes often require manual coding in pipeline development, which can be error-prone and require significant development time.

‍Solution: PyAirbyte reduces the need for custom ETL coding through a vast library of pre-built connectors. It allows you to effortlessly connect to various data sources like APIs, databases, and SaaS applications. This streamlined approach lets you quickly set up data pipelines and extract insights from your data.

Scalability

Problem: Scaling traditional ETL pipelines to handle growing data volumes can be challenging and often requires significant architectural changes.

‍Solution: PyAirbyte can handle large data volumes efficiently. It writes data to disk first and compresses it, ensuring fast and memory-efficient processing. Therefore, it is easier to manage increasingly complex data workflows.

Lack of Interoperability

Problem: Traditional ETL processes may face challenges when it comes to interoperability with other tools and frameworks. 

‍Solution: PyAirbyte overcomes this problem by ensuring compatibility with various Python libraries, such as Pandas and SQL-based tools. You can also integrate seamlessly with popular AI frameworks, such as LangChain and LlamaIndex, to develop LLM-powered applications.

Streamline Python ETL Workflows with PyAirbyte

Python gives you full control over building ETL pipelines—but manual coding comes with overhead. With PyAirbyte, you get the best of both worlds: the power of Python with the speed and simplicity of pre-built connectors.

From extraction to transformation and loading, PyAirbyte automates the heavy lifting while keeping your pipeline flexible and developer-friendly. You can integrate with tools like pandas, SQLAlchemy, or even LLMs—without sacrificing scale or performance.

Whether you're building your first ETL or scaling a production-grade pipeline, PyAirbyte helps you get there faster. Try it now and simplify your data integration in Python.

FAQ’s

Is Python good for ETL?

Yes, Python is well-suited for ETL tasks. It provides a wide range of libraries and frameworks that enable you to extract data from various sources easily, transform it as needed, and load it into different destinations.

Can we use pandas for ETL?

Yes, Pandas is widely used for ETL tasks. It provides robust data manipulation capabilities for cleaning, transforming, and analyzing data efficiently.

Should I use pandas or SQL for ETL?

The choice between pandas and SQL for ETL depends on the specific requirements of your project. If you are working with structured data and prefer a SQL-like approach, using SQL for ETL might be a good option. On the other hand, if you need more flexibility and advanced data manipulation, pandas can be a better choice.

Which IDE is best for ETL with Python?

There are several IDEs that are commonly used for ETL with Python, such as PyCharm, Jupyter Notebook, and Visual Studio Code. However, the best IDE depends on your personal preference and project requirements.

Python or SQL for ETL? Or both?

Both Python and SQL have their strengths for ETL. Python provides many libraries and tools for data manipulation and analysis, making it suitable for complex transformations. SQL, on the other hand, is well-suited for querying and manipulating structured data in databases. Depending on the task, you can use Python, SQL, or both to achieve your ETL goals.