Data Tokenizing | Dagster Glossary

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Data Tokenizing

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

Tokenization definition:

Tokenization is the process of breaking down a piece of text into individual words or tokens. This is a common technique used in data engineering to prepare text data for analysis.

Tokenizing example using Python:

Here are some practical examples of tokenization in data engineering using Python-specific functions. Please note that you need to have the necessary Python libraries installed in your Python environment to run this code:

Using the split() function: The split() function can be used to split a string into a list of words based on a delimiter, such as a space or a comma.

For example:

text = "This is a sample sentence."
tokens = text.split()
print(tokens)

This would output:

['This', 'is', 'a', 'sample', 'sentence.']

Using the word_tokenize() function from the NLTK library: The Natural Language Toolkit (NLTK) is a popular Python library for natural language processing. The word_tokenize() function from the NLTK library can be used to tokenize text data into individual words. For example:

import nltk
nltk.download('punkt')

from nltk.tokenize import word_tokenize

text = "This is a sample sentence."
tokens = word_tokenize(text)
print(tokens)

Will output:

['This', 'is', 'a', 'sample', 'sentence', '.']

Using regular expressions: Regular expressions can be used to define patterns for tokenizing text data. For example, the following code uses regular expressions to split a string into words based on whitespace and punctuation:

import re

text = "This is a sample sentence."
tokens = re.findall(r'\b\w+\b', text)
print(tokens)

This code would produce the following output in the terminal:

['This', 'is', 'a', 'sample', 'sentence']

Using the split() function with a regular expression pattern: The split() function can also be used with regular expression patterns to tokenize text data. For example:

import re

text = "This is a sample sentence."
tokens = re.split('\W+', text)
print(tokens)

This will yield:

['This', 'is', 'a', 'sample', 'sentence', '']

These are just a few examples of how tokenization can be used to prepare data for analysis and extract insights from text.


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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Munge

See 'wrangle'.
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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.

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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