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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()

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'punkt')

from nltk.tokenize import word_tokenize

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

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)

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)

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:


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.


An imbalance in the distribution or representation of data.


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


Convert data from one format or structure to another.


Convert unstructured data into a structured format.