PySpark - Replace Accented letters with their Non-Accented letters

In this tutorial, you will learn "How to Replace Accented letters with their non accented letters By Using PySpark" in DataBricks. For this I used PySpark runtime.

Data integrity refers to the quality, consistency, and reliability of data throughout its life cycle. Data engineering pipelines are methods and structures that collect, transform, store, and analyse data from many sources.

If you are working as a PySpark developer, data engineer, data analyst, or data scientist for any organization requires you to be familiar with dataframes because data manipulation is the act of transforming, cleansing, and organising raw data into a format that can be used for analysis and decision making.

🚀The unicodedata module in Python provides access to the Unicode Character Database, which defines various properties of Unicode characters. This module can be particularly useful for tasks such as removing accents from characters, normalizing text, and working with Unicode properties.

🚀Requested Task - Write a PySpark solution to replace the accented letters with Non-Accented letters in the Non-Accented_Name column.

Data Cleansing OR Data Scrubbing Process 🚀Significantly impacts the quality, 🚀Efficiency, 🚀Effectiveness of data utilization, 🚀Ensuring data is accurate, 🚀Consistent, and Compliant, 🚀Facilitating a unified view of the information,
🚀Enhancing overall data interoperability, 🚀Foundation for Robust Data Analytics and 🚀Root for Reliable Decision-Making

0:00 Introduction 1:30 Import PySpark Libraries and Compute Cluster 2:50 Create UDF Function 3:52 Build Sample data with Accented Characters 5:00 Call UDF Function to Replace Accented letters into Non-Accented_Name column

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