DataBricks — Retrieve matched rows from two Dataframes or Files by Using PySpark

In this tutorial, you will learn "How to Retrieve matched rows from two Dataframes or Files 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.

Requested Task - You are given two data files and build a new dataframe from those two files based on the match rows on a common called by using inner join. 🚀Only those records based on matching key column 🚀Pull all matching columns into a new dataframe

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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