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Data Purging

Delete data that is no longer needed or relevant to free up storage space.

Data purging definition:

Data purging is the process of permanently deleting data from a system or database. This process is important in modern data pipelines to ensure that data that is no longer needed or is outdated is removed from the system to free up storage space and reduce security risks.

Data purging example using Python:

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

In Python, data purging can be achieved using various libraries and frameworks. For example, the following code demonstrates how to use the Pandas library to delete rows containing null values in a DataFrame:

import pandas as pd

# create a sample DataFrame
data = {'name': ['Alice', 'Bob', 'Charlie', 'David'],
        'age': [25, 30, None, 40],
        'city': ['New York', 'Paris', None, 'London']}
df = pd.DataFrame(data)

# remove rows with null values
df = df.dropna()

# print the updated DataFrame

In the above example, the dropna() method is used to remove all rows with null values in the DataFrame. This method removes any row that contains at least one null value. The resulting DataFrame will only contain the rows that have complete data.

The above example would purge the record for Charlie and yield the output:

    name   age      city
0  Alice  25.0  New York
1    Bob  30.0     Paris
3  David  40.0    London

It is important to note that data purging should be done with caution, as it is a permanent process and may lead to the loss of valuable information. It is recommended to backup the data before purging and to carefully consider the retention policy for the data.

Other data engineering terms related to
Data Security and Privacy: