DataBricks — Transpose OR Pivot OR Rows to Columns in Dataframe By Using PySpark

In this tutorial, you will learn "Transpose OR Pivot OR Rows to Columns 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.

0:00 Introduction 0:37 Import PySpark Libraries and Compute Cluster 2:40 Sample data from DBFS 3:30 Load data from DBFS to PySpark Dataframe 4:22 Add Multiple Columns - SalesAmt and Discount 6:10 Transpose OR Pivot OR Rows to Columns conversion

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