DataBricks — How to apply Data Cleansing in Dataframe By Using PySpark

In this tutorial, you will learn "How to apply Data Cleansing in Dataframe 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 organisation 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.

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 0:29 Import PySpark Libraries and Compute Cluster

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