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An imbalance in the distribution or representation of data.

Unlike most of the entries in our glossary, skew is not something engineers do to data, but a characteristic of the data itself.

Data skew definition:

Data skew, also known as skewness, refers to an imbalance in the distribution or representation of data. In the context of distributed computing, this imbalance often leads to inefficient processing because some nodes in the system end up processing significantly more data than others.

This can occur in several ways. For example, one common type of skew is key-value skew, where some keys in your data map to significantly more values than others. If your processing tasks involve these keys, they can take much longer to process than tasks involving other keys. This imbalance can lead to an overall slowdown in your pipeline because other nodes may be sitting idle while a few nodes are still processing.

Another type of skew is time skew, where certain time periods have significantly more data than others. This can also lead to imbalances and inefficiencies in your pipeline if not accounted for.

Strategies for dealing with data skew can involve redistributing the data, adjusting the granularity of tasks, using more robust statistics or models that can handle skew, or pre-processing the data to balance out the skew.

Illustrating skewness using Python

We can illustrate the concept of data skewness using two distributions, one normal (symmetrical) and another skewed.

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

Here's how you can do it using matplotlib and scipy.stats:

import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats

# Create a normal distribution
mu, sigma = 0, 0.1 # mean and standard deviation
normal_dist = np.random.normal(mu, sigma, 1000)

# Create a skewed distribution
skewness = 5  # positive value means right skew
skewed_dist = stats.skewnorm.rvs(a = skewness, size=1000)

# Create subplots
fig, axs = plt.subplots(2, sharex=True)

# Plot normal distribution
axs[0].hist(normal_dist, bins=30, density=True, color='b', alpha=0.7)
axs[0].set_title('Normal Distribution')

# Plot skewed distribution
axs[1].hist(skewed_dist, bins=30, density=True, color='r', alpha=0.7)
axs[1].set_title('Skewed Distribution')

In this example, we first create a normally distributed set of data, with mean 0 and standard deviation 0.1. We then create a skewed distribution using scipy.stats.skewnorm. The a parameter controls the skewness of the distribution, with positive values indicating right-skew.

Then we plot histograms of these two distributions. The top blue 'Normal Distribution' histogram should show a symmetric bell curve, while the bottom red 'Skewed Distribution' histogram will be skewed to the right.

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.


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


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.


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.