Introduction
Data is the lifeblood of today's digital economy, driving important business decisions. However, data often arrives in formats that aren't immediately suitable for analysis. Data transformation is the process of converting data from one format or structure into another to make it more suitable for operations such as analytics.
This article breaks down the what, why, and how of data transformation, along with practical examples.
What is Data Transformation?
Data transformation refers to the process of changing the format, structure, or values of data. It involves the conversion of data from one form or format to another, making it more understandable, usable, and effective for tasks such as data analysis. Data transformation can involve several operations such as cleaning, normalization, aggregation, and integration.
For instance, consider a simple example where a database stores temperatures in Fahrenheit, but for your data analysis, you need them in Celsius. You would perform a data transformation operation to convert these values.
def fahrenheit_to_celsius(fahrenheit):
return (fahrenheit - 32) * 5.0/9.0
temperature_fahrenheit = 75
temperature_celsius = fahrenheit_to_celsius(temperature_fahrenheit)Also see, Mercurial
Why is Data Transformation Important?
Data transformation is vital for several reasons:
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Data Cleaning: Data can have inconsistencies, redundancies, or errors. Transformation helps clean data by removing or correcting these inaccuracies.
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Data Integration: Data transformation allows integration of data from multiple sources, each possibly having a different format or structure.
- Improved Data Analysis: By transforming data into a suitable format, data analysis becomes more efficient and effective.
The Process of Data Transformation
Data transformation typically involves four stages:
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Data Discovery: The first stage involves understanding the type, structure, and quality of the source data.
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Data Mapping: In this stage, the transformation rules are defined to convert data from its source format to the target format.
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Code Generation: The transformation logic is implemented in this stage, often using a programming language or a data transformation tool.
- Data Delivery: Finally, the transformed data is loaded into the target system or database.
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