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Interpret and convert data from one format to another.

Data parsing definition:

Data parsing is the process of interpreting and converting data from one format to another. It involves taking data, typically structured in a specific manner such as CSV, JSON, XML, etc., and transforming it into a format that is more useful or easier to process and analyze. This process usually involves a set of rules or patterns to understand the input data and to determine how the transformation should occur. Data parsing is a common task in areas like data preprocessing, web scraping, and data integration.

Data parsing example using Python:

Let's look at an example of parsing CSV (Comma-Separated Values) data, which is a common task in data analysis. Please note that you need to have the 'csv' library installed in your Python environment to run this code.

Assume we have a CSV file named data.csv with the following content:


Here is a Python script that parses this file using the csv module. Please ensure that the 'data.csv' file is in the same directory as your Python script, or provide an absolute path to the file.

import csv

def parse_csv_file(file_name):
    with open(file_name, 'r') as file:
        reader = csv.reader(file)
        header = next(reader)  # This will store the header row (['Name', 'Age', 'Occupation'])
        rows = []
        for row in reader:
            # Convert each row to a dictionary with keys as the header and values as the row values
            rows.append(dict(zip(header, row)))
        return rows

data = parse_csv_file('data.csv')

for person in data:
    print(f"Name: {person['Name']}, Age: {person['Age']}, Occupation: {person['Occupation']}")

This script first opens the file and creates a CSV reader. It then stores the header row (which contains the field names) and iterates over the rest of the rows, converting each one to a dictionary where the keys are the field names and the values are the corresponding values from the row. This way, you get a list of dictionaries where each dictionary represents a person.

Run the script, and your parsed data will show as follows:

Name: Alice, Age: 30, Occupation: Engineer
Name: Bob, Age: 25, Occupation: Doctor
Name: Charlie, Age: 35, Occupation: Teacher

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.


Divide data into smaller subsets for improved performance.


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


Transform raw data before data analysis or machine learning modeling.


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.