Data Munging | Dagster Glossary

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Data Munging

See 'wrangle'.

Definition of data munging

The process of converting unstructured data into a structured format is typically referred to as "data wrangling" or "data munging". Both terms refer to the process of transforming and mapping data from a raw form into another format that allows for more convenient consumption of the data.

However, different people and organizations may use these terms in slightly different ways. For example, some people may use "data munging" to specifically refer to the cleaning and preprocessing of data, while "data wrangling" might be used in a broader sense to include tasks such as gathering and exploring the data.


Other data engineering terms related to
Data Transformation:
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Align

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

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

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

Select, organize, and annotate data to make it more useful for analysis and modeling.
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Denoise

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

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

Extracting, transforming, and generating new data from existing datasets.
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Discretize

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

Extract, transform, and load data between different systems.
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Encode

Convert categorical variables into numerical representations for ML algorithms.
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Filter

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

Break data down into smaller chunks for storage and management purposes.
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Homogenize

Make data uniform, consistent, and comparable.
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Impute

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

Transforming the relationship between variables to make datasets approximately linear.
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Normalize

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

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

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

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

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

An imbalance in the distribution or representation of data.
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Split

Divide a dataset into training, validation, and testing sets for machine learning model training.
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Standardize

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

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

Convert data from one format or structure to another.
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Wrangle

Convert unstructured data into a structured format.
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