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Transform raw data before data analysis or machine learning modeling.

Data preprocessing definition:

Data Preprocessing is a fundamental step in any data science or data engineering project.

Data preprocessing is a data mining technique that transforms raw data into an understandable and efficient format. This step typically takes place before the data analysis or machine learning modeling stages. Data preprocessing may involve several processes like:

  • Data Cleaning: Removing noise and handling missing or inconsistent data. This step ensures that the quality of data is good enough for the subsequent stages.

  • Data Transformation: Normalization, aggregation, or general scaling of data to prepare it for the machine learning algorithms.

  • Data Reduction: Reducing the volume but producing the same or similar analytical results.

  • Data Discretization: The process of converting continuous data to categorical data.

  • Feature Extraction or Feature Selection: The process of creating new features or modifying existing features to improve the performance of machine learning models.

A simple example of data preprocessing using Python:

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

Here is a simple example of text preprocessing. This script uses NLTK and re (regular expressions) to preprocess a simple text string. The preprocessing tasks include:

  1. Text lowercase
  2. Remove numbers
  3. Remove punctuation
  4. Remove whitespaces
  5. Remove stopwords
  6. Stem (reduce words to their root form.)
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer'punkt')'stopwords')

# Define the text string
text_string = "Rein in the chaos and maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any of 100 issues and identify performance improvement opportunities."

# Step 1: Convert text to lowercase
text_string = text_string.lower()

# Step 2: Remove numbers
text_string = re.sub(r'\d+', '', text_string)

# Step 3: Remove punctuation
text_string = re.sub(r'[^\w\s]', '', text_string)

# Step 4: Remove whitespaces
text_string = text_string.strip()

# Step 5: Remove stopwords
stop_words = set(stopwords.words('english'))
tokens = nltk.word_tokenize(text_string)
tokens = [token for token in tokens if token not in stop_words]

# Step 6: Stemming
porter = PorterStemmer()
stemmed = [porter.stem(word) for word in tokens]

print(f"Original Text: {text_string}")
print("Preprocessed Text: ", ' '.join(stemmed))

If you execute this code you will see the following output:

Original Text: rein in the chaos and maintain control over your data as the complexity scales centralize your metadata in one tool with builtin observability diagnostics cataloging and lineage spot any of  issues and identify performance improvement opportunities
Preprocessed Text:  rein chao maintain control data complex scale central metadata one tool builtin observ diagnost catalog lineag spot issu identifi perform improv opportun

Data preprocessing vs. Data Preparation:

Data preparation is a broader term than data preprocessing that includes all activities to construct and consolidate a dataset for analysis or operational use. This can involve:

  • Data Acquisition: Collecting or integrating data from various sources.

  • Data Integration: Combining data from different sources and providing users with a unified view of the data.

  • Data Preprocessing: As described above, which includes cleaning, transformation, reduction, etc.

  • Data Partitioning: Splitting the data into training, validation, and test sets for machine learning models.

In essence, data preprocessing can be seen as a subset of data preparation. While data preparation encompasses the entire journey from raw data to the ready-for-analysis or operational-use data, data preprocessing is specifically concerned with refining and transforming the data after it's been acquired and integrated, and before it's analyzed or fed into a machine learning model. Both steps are critical to ensuring the accuracy, reliability, and efficiency of the downstream processes and outcomes.

Other data engineering terms related to
Data Management:


Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. store data for long-term retention and compliance.


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


Create a copy of data to protect against loss or corruption.


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


Identify and remove duplicate records or entries to improve data quality.


Analyzing the number of features or attributes in the data to improve performance.


Enhance data with additional information from external sources.


Extract data from a system for use in another system or application.


Create an optimized data structure for fast search and retrieval.


combine data from different sources to create a unified view for analysis or reporting.


Store the results of expensive function calls and reusing them when the same inputs occur again.


Combine data from multiple datasets into a single dataset.


Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.


Create a conceptual representation of data objects.


Track data processing metrics and system health to ensure high availability and performance.

Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.


Interpret and convert data from one format to another.


Divide data into smaller subsets for improved performance.


Transform your data so it is fit-for-purpose.


Create a copy of data for redundancy or distributed processing.


Increasing the capacity or performance of a system to handle more data or traffic.

Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.


Ensure that data in different systems or databases are in sync and up-to-date.


Check data for completeness, accuracy, and consistency.


Maintain a history of changes to data for auditing and tracking purposes.