Data pipelines with Python "how to" - A comprehensive guide

Data is the backbone of today's digital world and the effective management of this flood of information is crucial to the success of companies and organizations. Python has become a popular language for handling data thanks to its simplicity and flexibility. In this comprehensive guide, you will learn how to create, manage and optimize data pipelines with Python.

We discuss the best practices, libraries and frameworks for creating Python data pipelines and provide examples to help you get started with your own projects.

This post is suitable for tech-savvy readers. You can find a general introduction to data pipelines here: General introduction to Data Pipelines

What is a data pipeline in Python?

A data pipeline with Python is a series of data processing steps that transform raw data into actionable insights. This includes the

  • Collect,
  • Clean up,
  • Validate and
  • Convert

of data to make it suitable for analysis and reporting. Data pipelines in Python can be simple and consist of a few steps - or they can be complex and include several steps and tools. Both are possible.

Data Pipeline Python
Example graphic of a data pipeline in Python

Python data pipelines frameworks

Python provides several frameworks for creating data pipelines, including Apache Airflow, Luigi, and Prefect. With these frameworks, you can easily create, schedule, and manage your data pipelines.

  • Apache Airflow: A powerful open source platform that allows you to create, plan and monitor workflows in Python.
  • Luigi: A Python module developed by Spotify that simplifies the construction of complex data pipelines.
  • Prefect: A modern data pipeline framework with a focus on simplicity, flexibility and scalability.

Building a data pipeline with Python - examples and best practices

To create a data pipeline with Python, follow the step-by-step instructions.

Data is a precious thing and will last longer than the systems themselves.

Tim Berners-Lee

5 steps for data processing in English with German Anglicisms:

  1. Define the data sources: Identify where the data comes from and how it should be collected.
  2. Clean and validate data: Use Python libraries such as Pandas and NumPy to clean, validate and prepare the data.
  3. Transform and enrich data: Use Data transformations and enrichments to improve the quality of the data for analysis.
  4. Store the processed data: Save the processed data in a suitable storage system, such as a database or a Cloud storage.
  5. Analyze and visualize data: Use Python libraries such as Matplotlib, Seaborn and Plotly for Data visualization and analysis.

Here are 4 helpful tips to improve your data pipeline:

  1. Modularize your code: Break your pipeline into smaller, reusable components to make it easier to maintain and debug.
  2. Use version control: Track changes to your pipeline code and data using tools such as Git and GitHub.
  3. Automate testing: Implement automated Teststo ensure the accuracy and integrity of your data pipeline.
  4. Monitor and log: Set up monitoring and logging systems to track the performance and health of your data pipeline.

Pipelines for streaming data in Python

Python can be used to build real-time pipelines for streaming data, processing data as it is generated. With libraries such as Kafka-Python, Faust and Streamz, it is possible to create streaming data pipelines to process large amounts of data in real time.

Pipeline libraries for data processing

Python provides a rich ecosystem of libraries for building data processing pipelines.

Data is the new oil and you need good tooling to retrieve it

Adaptation from Clive Humby "Data is the new oil"

Here are some important libraries for data manipulation and analysis in Python:


A powerful library for data manipulation and analysis. With Pandas, data can be imported in various formats such as CSV, Excel or SQL tables and saved as data frames (DataFrame). Pandas also offers many functions for data manipulation such as filtering, grouping and aggregation.


A library for numerical calculations in Python. NumPy offers a variety of functions for numerical calculations such as linear algebra, Fourier transformation and random number generation. NumPy is also the basis for many other libraries used in data science.


A parallel computing library for large-scale data processing. With Dask you can process large data sets in parallel on a cluster of computers. Dask also offers functions for storing and analyzing large data sets in distributed systems.


A library for machine learning and data mining in Python. Scikit-learn offers a variety of machine learning algorithms such as regression, classification, clustering and dimensionality reduction. Scikit-learn also offers functions for data modeling, evaluation and selection.

As Clive Humby said: "Data is the new oil"

and these libraries help to gain valuable knowledge and insights from this data.

Extract, transform, load (ETL) is a common approach to creating data pipelines. Python is an excellent choice for creating ETL pipelines because of its extensive library support and ease of use. Some popular Python libraries for ETL are Pandas, SQLAlchemy, and PySpark.

Data pipelines for machine learning with Python

Python is widely used to create data pipelines for machine learning. Libraries such as TensorFlow, Keras, and PyTorch provide powerful tools for building and training machine learning models, while Scikit-learn offers a comprehensive suite of machine learning algorithms and data preprocessing tools.

Data pipeline architecture with Python

When designing your data pipeline architecture in Python, you should consider the following components:

  • Data Ingestion: Identify the sources of your data and create processes to collect and capture it.
  • Data Storage: Choose appropriate storage systems such as databases or data storage systems to store your raw and processed data.
  • Data Processing: Design and implement data processing tasks such as cleansing, validation, transformation, and enrichment.
  • Data Analysis and Visualization: Implement data analysis and visualization tasks using Python libraries such as Matplotlib, Seaborn, and Plotly.
  • Data Orchestration and Scheduling: Use data pipeline frameworks such as Apache Airflow or Luigi to schedule and manage your data processing tasks.

Object Oriented Data Science - Python Data Processing Pipeline

Using an object-oriented approach when building your data processing pipeline in Python improves the modularity, maintainability and reusability of the code. Define classes and methods for each phase of your data pipeline and encapsulate the logic and data in each class. This approach promotes separation of concerns and makes it easier to test and maintain your pipeline.

Here is an example of a Python data pipeline as a Python class:

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
class DataPipeline:
    def __init__(self, data_path):
        self.data_path = data_path = None
        self.scaled_data = None
        self.pca_data = None
    def load_data(self): = pd.read_csv(self.data_path)
    def scale_data(self):
        scaler = StandardScaler()
        self.scaled_data = scaler.fit_transform(
    def perform_pca(self, n_components):
        pca = PCA(n_components=n_components)
        self.pca_data = pca.fit_transform(self.scaled_data)
    def run_pipeline(self, n_components):

In this example the class DataPipeline three methods: load_data(), scale_data() and perform_pca().

The method load_data() loads the data from a CSV file specified by the parameter data_path is specified.

The method scale_data() standardizes the data using the class StandardScaler from the module sklearn.preprocessing.

The method perform_pca() performs a principal component analysis (PCA) on the scaled data using the class PCA from the module sklearn.decomposition by.

The method run_pipeline() is used to execute the data pipeline by successively executing each of the three methods with the specified parameter n_components for the PCA.

To use this data pipeline, you can create an instance of the class DataPipeline create and the method run_pipeline() call

pipeline = DataPipeline('data.csv')

This loads the data from the file data.csv, scales the data, performs PCA with 2 components, and stores the resulting PCA-transformed data in the attribute pca_data of the pipeline-Object.

Simple data pipeline with Python "how to"

To create a simple data pipeline in Python, follow these steps:

  1. Use simple Python scripts for small data processing tasks.
  2. Use built-in Python libraries like CSV and JSON for basic data preparation.
  3. Access high-level libraries such as Pandas and NumPy for more complex data processing.
  4. Use Jupyter Notebook or Google Colab for fast prototyping and visualization.

Data pipeline tools and techniques in Python

Some additional tools and techniques to help you create robust and efficient data pipelines in Python include:

  • Data Quality: Implement data validation and cleaning techniques to ensure the integrity of the data pipeline.
  • Pipeline integrity: Monitor the performance and health of your data pipeline to quickly identify and resolve issues.
  • Data visualization: Use Python libraries such as Matplotlib, Seaborn and Plotly to create visually appealing and informative graphs and diagrams.
  • Data pipeline optimization: Parallel processing, caching and other performance-enhancing techniques are ways of optimizing the data pipeline.

Python Example

This example illustrates how you can use Pydantic and Luigi to create a simple data pipeline in Python and how it reads, validates and processes the data from a CSV file and then writes the converted data to a new CSV file.

Computers are good at following instructions, but not at reading your mind.

Donald Knuth

Pydantic and Luigi - A sample pipeline in Python

To run the example pipeline in Python, you first need to install the libraries:

pip install pydantic luigi pandas

A file named should then be created to define a Pydantic model for data validation:

from pydantic import BaseModel
class UserData(BaseModel):
    id: int
    name: str
    age: int
    email: str

Now a file called should be created, in which Luigi tasks for reading, processing and writing data are defined:

import luigi
import pandas as pd
from models import UserData
class ReadCSV(luigi.Task):
    input_file = luigi.Parameter()
    def output(self):
        return luigi.LocalTarget("intermediate.csv")
    def run(self):
        df = pd.read_csv(self.input_file)
        df.to_csv(self.output().path, index=False)
class ProcessData(luigi.Task):
    input_file = luigi.Parameter()
    def requires(self):
        return ReadCSV(input_file=self.input_file)
    def output(self):
        return luigi.LocalTarget("output.csv")
    def run(self):
        df = pd.read_csv(self.requires().output().path)
        # Validate and process data using Pydantic
        processed_data = []
        for index, row in df.iterrows():
                user_data = UserData(**row.to_dict())
            except ValueError as e:
                print(f "Skipping invalid row: {e}")
        # Write processed data to a new CSV file
        processed_df = pd.DataFrame(processed_data)
        processed_df.to_csv(self.output().path, index=False)
if __name__ == "__main__":[ProcessData(input_file="input.csv")], local_scheduler=True)

In this example, ReadCSV reads the input CSV file and writes it to an intermediate file. The ProcessData task reads the intermediate CSV file, validates and processes the data using the Pydantic UserData model, and writes the transformed data to the output CSV file.

To run the pipeline, a sample Input.csv file should be created:

1,Alice,30,[email protected]
2,Bob,25,[email protected]
3,Charlie,22,[email protected]

After that, the script should be executed:


The script creates an output CSV file that contains the validated and processed data. Note that this is a simple example and you can customize the pipeline to your needs by adding more complex data processing and validation logic.

Conclusion on the creation of data pipelines with Python

Creating data pipelines with Python is an essential skill for data professionals. This comprehensive guide provides an overview of the key concepts, tools and best practices for creating effective and efficient data pipelines. By leveraging Python's rich ecosystem of libraries, frameworks, and tools, you can develop data pipelines that turn raw data into valuable insights, enabling you to make data-driven decisions and drive your organization's success.

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