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Convert a large set of data into a smaller, more manageable form without significant loss of information.

Data reduction definition:

Data Reduction in data engineering refers to the process of transforming, encoding, or otherwise converting a large set of data into a smaller, more manageable form without significant loss of information. This is usually done to increase efficiency, reduce data storage requirements, and improve the speed of subsequent data processing tasks.

Methods for data reduction can include dimensionality reduction techniques, data compression, data sampling, binning, feature selection, feature extraction, and aggregation, among others. The aim is to maintain the data's integrity and usefulness while making it more manageable.

Data reduction is especially crucial in big data applications, where the sheer volume of data can become a hindrance to data processing and analysis. By intelligently reducing data size, we can optimize storage, improve performance, and make the data more understandable.

Data reduction example in Python:

Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

Let's look at a simple data reduction method by applying a PCA (Principal Component Analysis) technique to a sample dataset. In a modern data pipeline, we often need to reduce the dimension of data, especially for high-dimensional data, to save storage and computational power.

We'll be using the famous Iris dataset from sklearn for the demonstration. This data set has four features, and we'll reduce it to two features using PCA.

Here's an example:

from sklearn.decomposition import PCA
from sklearn.datasets import load_iris
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the iris dataset
iris = load_iris()
X =
y =

# Original dataset dimensions
print(f"Original dataset shape: {X.shape}")

# Create the PCA object, specifying the number of components (or dimensions) to reduce to
pca = PCA(n_components=2)

# Fit and apply the PCA to the data (this is done in one step)
X_pca = pca.fit_transform(X)

# Reduced dataset dimensions
print(f"Reduced dataset shape: {X_pca.shape}")

# Convert numpy array to pandas dataframe
df = pd.DataFrame(data = X_pca, columns = ['Principal Component 1', 'Principal Component 2'])
df['target'] = y

# Visualize 2D Projection
sns.scatterplot(x='Principal Component 1', y='Principal Component 2', hue='target', data=df, palette='Set1')

In this script, we load the iris data set, which has 4 features. Then, we use PCA to reduce it to two features. We then plot these two features as a scatter plot to visualize the reduced data, which produces a graph as follows:

It will print out to the terminal:

Original dataset shape: (150, 4)
Reduced dataset shape: (150, 2)

This script demonstrates a fundamental data reduction technique that is commonly used in modern data pipelines, particularly when dealing with high-dimensional data.

Other data engineering terms related to
Data Transformation:


Aligning data can mean one of three things: aligning datasets, meeting business rules or arranging data elements in memory.

Big Data Processing

Process large volumes of data in parallel and distributed computing environments to improve performance.

Clean or Cleanse

Remove invalid or inconsistent data values, such as empty fields or outliers.


Group data points based on similarities or patterns to facilitate analysis and modeling.


Remove noise or artifacts from data to improve its accuracy and quality.


Optimize data for faster read access by reducing the number of joins needed to retrieve related data.


Transform continuous data into discrete categories or bins to simplify analysis.


Extract, transform, and load data between different systems.


Extract a subset of data based on specific criteria or conditions.


Convert data into a linear format for efficient storage and processing.


Fill in missing data values with estimated or imputed values to facilitate analysis.


See 'wrangle'.


Standardize data values to facilitate comparison and analysis. organize data into a consistent format.


Change the structure of data to better fit specific analysis or modeling requirements.


Convert data into a linear format for efficient storage and processing.


Break down large datasets into smaller, more manageable pieces for easier processing and analysis.


An imbalance in the distribution or representation of data.


Transform data to a common unit or format to facilitate comparison and analysis.


Convert data into tokens or smaller units to simplify analysis or processing.


Convert data from one format or structure to another.


Convert unstructured data into a structured format.