avatarRandy Geszvain


Big Data Pipeline Using Kedro and PySpark

In today’s data-driven world, businesses are dealing with ever-growing data sets. The complexity of data continues to increase, and so do the challenges of managing, processing, and analyzing it. It is where the Big Data Pipeline comes into the picture. Big Data Pipeline is a set of tools and techniques that help manage, process, and analyze large and complex data sets. Kedro and PySpark can help build a Big data pipeline in this context. Kedro is an open-source data pipeline framework that helps create production-ready data pipelines. PySpark, on the other hand, is a robust data processing framework that provides a fast and efficient way to scale data engineering and data science projects.

(Below contents are enhanced by AI)

Technology Stack


Kedro is an open-source data pipeline framework that helps create production-ready data pipelines. A Kedro pipeline is a set of interconnected nodes that represent the stages of data processing. Each node in the pipeline represents a data processing step, such as reading data from a source, transforming it, and writing it to a destination. The nodes are organized in a directed acyclic graph (DAG) that defines the order of execution. The Kedro pipeline is designed to be modular and scalable, so you can easily add new nodes to the pipeline as your data processing needs evolve. Overall, Kedro simplifies the process of building complex data pipelines by providing a consistent, reproducible, and scalable framework.



PySpark is the Python library for Apache Spark, a powerful open-source processing engine for big data. PySpark allows you to write Spark applications using Python instead of Scala or Java. It provides a simple and easy-to-use API that allows you to distribute data processing tasks across a cluster of computers. With PySpark, you can process large amounts of data in parallel, making it an ideal tool for big data processing. It also provides support for machine learning, graph processing, and real-time data processing.



Pydantic is a Python library providing runtime validation and data structure parsing. It is primarily designed to help validate and serialize complex data systems. Pydantic provides a simple way to define Python classes that represent data structures, and it automatically validates and parses input data against these classes. The process makes it easy to create and maintain data models in Python and ensures that the data being processed is valid and well-formed. Pydantic is also highly configurable and can be used with various data formats, making it a versatile tool for data processing.


MongoDB is a popular open-source document-oriented NoSQL database program. It is designed to store unstructured data in JSON-like documents. It is famous for handling large volumes of semi-structured and unstructured data, particularly in big data and real-time analytics contexts. MongoDB supports a flexible data model, dynamic schema, and high scalability, allowing users to store and process data in a distributed environment across multiple servers and data centers. It is commonly used by businesses of all sizes, including startups and large enterprises, to manage and store data for various applications, including e-commerce, mobile applications, social networks, and content management systems


Data Pipeline

Welcome to our graph explaining a Kedro Big Data Pipeline! In today’s data-driven world, businesses are dealing with ever-growing data sets, and managing, processing, and analyzing this data has become more complex. It is where Kedro, an open-source data pipeline framework, comes into the picture. In this graph, we will explore how Kedro can be used to build a production-ready pipeline for managing data from API calls, storing it in databases, preprocessing it with PySpark, normalizing API responses using Pydantic, and writing it to MongoDB. So, let’s dive into the Kedro Big Data Pipeline world and understand how it can simplify managing complex data sets.



Imagine you’re building a Big data pipeline and trying to figure out how to connect all the pieces. It’s like building a giant machine with different parts that must work together seamlessly. That’s where nodes and functions come into play.

In this code section, you can see how the pipeline is formed using different nodes and functions. Think of each node as a step in the pipeline, representing a specific processing task. And the functions are like little workers, each with their job at each step.

Different functions are executed at each step as the data moves through the pipeline, transforming the data into the desired format for the next step. It’s like a conveyor belt with workers at each station, each doing their part to prepare the product for the next production stage.

At the end of the pipeline, the output is stored in the preferred format for the next step. It’s like putting the finished product in a box, ready to be shipped out to the next stage of production.

So, with these nodes and functions working together, you can build a robust and efficient Big Data Pipeline that can handle even the most complex and massive datasets.

Nodes and Functions

The Kedro pipeline is a robust framework that offers several functions to process and manage complex data sets. However, PySpark comes to the rescue to scale up the performance and stability of the pipeline. With PySpark, the pipeline can distribute data processing tasks across a cluster of computers, ensuring speedy and efficient data processing.

But wait, there’s more! The Pydantic package is another gem that the Kedro pipeline can use to its advantage. It provides runtime validation and data structure parsing capabilities, enabling efficient transformation and normalization of JSON objects into a structured format. This normalized data can then be merged and further processed for insightful results.

And what do we do with these results, you ask? We store them in a MongoDB NoSQL database, of course! MongoDB is designed to handle semi-structured and unstructured data, making it an ideal fit for storing the results of the Kedro pipeline. The database’s flexible data model, dynamic schema, and high scalability mean that real-time data analytics can be performed quickly and efficiently, making it a perfect fit for businesses of all sizes looking to manage and store large volumes of complex data.

Happy Coding!

Big Data
Data Engineering
Data Science
Recommended from ReadMedium