This device is not compatible.


Create Your First Data Pipeline with a Dashboard

We’ll teach you how to create a data pipeline and interactive data visualization in Python. We’ll begin by building a bespoke data pipeline with Kedro and then utilize hvPlot to display the findings as interactive graphs.

Create Your First Data Pipeline with a Dashboard

You will learn to:

Create the data preprocessing and data transformation pipelines

Apply multiple levels of transformations on data

Visualize data to draw conclusions

Add interactivity to visualizations


Data Science

Data Visualisation

Data Manipulation

Data Pipeline Engineering


Basic programming in Python

Basic knowledge of Pandas

Basic knowledge of data pipelines

Basic knowledge of plotting in Python





Project Description

According to the Statista 2022 analysis, the quantity of data generated, recorded, replicated, and consumed globally is predicted to skyrocket to 181 zettabytes from 2021 to 2025:

The volume of data generated, recorded, replicated, and consumed globally in zettabytes (2010-2025)

Data is meaningless without professionals who convert it into valuable insights. Here are a few examples of how a data scientist offers value to a company:

  • They allow a business to make better decisions based on the insights.
  • They suggest actionable goals based on trends that will help the company flourish.
  • They determine new opportunities and make judgments based on measurable, data-driven evidence, and evaluate these conclusions using consumer insights.
  • They perform target audience identification and refinement based on buyer trends.

As a result, modern organizations are awash in data which necessarily involves data processing and analysis. In this project, we’ll learn to create a data pipeline and interactive data visualization in Python using Kedro and hvPlot to get valuable insights from our data.

  • Kedro is an open-source Python framework for creating systematic, reusable, and modular data pipelines.
  • hvPlot is a high-level plotting API based on HoloViews. It offers an alternative to the static plotting API offered by Matplotlib and other libraries. It provides numerous interactive features such as panning, zooming, hovering, clickable/selectable axis, legends, and so on.

We will begin by building a data pipeline from scratch with Kedro. We’ll introduce data preprocessing in our data pipeline along the way and run our data pipeline successfully. We’ll get an overview of the static and interactive plots with Pandas and hvPlot, respectively. Then, we’ll transform the processed data further and display our findings as interactive plots.

Project Tasks


Star the Data Pipeline

Task 1: Load the Raw Data

Task 2: Create the First Node

Task 3: Create a Data Preprocessing Node

Task 4: Use the Data Catalog

Task 5: Design the Data Pipeline

Task 6: Run the Data Pipeline


Set Up Interactive Plotting

Task 7: Create the Static Plots With Pandas

Task 8: Create Dynamic Plots with hvPlot

Task 9: Create the Dynamically Filtered KDEs Using hvPlot


Perform Advanced Data Manipulations in the Pipeline

Task 10: Create a Node for Data Transformation

Task 11: Modify the Data Catalog

Task 12: Run the Data Pipeline With Recently Created Node


Enhanced Interactive Plots with hvPlot

Task 13: Load the Transformed Data

Task 14: Plot a KDE: Hourly Temperatures for Individual Classes

Task 15: Plot a KDE: Hourly Wind Speeds for Individual Classes