Databricks and PySpark | Full Course | - UNION vs UNIONBYNAME in PySpark using Databricks | Merge two dataframes in PySpark |

Hello Geeks, If you are new to GeekCoders platform and want to complete End to End project then here is the link to the course. https://www.geekcoders.co.in/courses/Build-End-to-End-Azure-Data-Engineering-Project-644fba73e4b0a1072ff43a72-644fba73e4b0a1072ff43a72 Get the code from here Notebook link: https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/7123152967614832/2072394139685780/8592719976039731/latest.html If you are new to this playlist then please watch out the below playlist completely. https://www.youtube.com/watch?v=uvrrN8aqZW0&list=PLxy0DxWEupiNjGSv1hzRFBXgSzV-bZu94 In PySpark, there is no built-in method called `𝐮𝐧𝐢𝐨𝐧𝐛𝐲𝐧𝐚𝐦𝐞`. However, I can explain the difference between `𝐮𝐧𝐢𝐨𝐧` and `unionByName` methods available in PySpark. 1. `𝐮𝐧𝐢𝐨𝐧`: - The `union` method is used to combine two PySpark DataFrames with the same schema. It appends the rows of one DataFrame to another, creating a new DataFrame. - The schema of both DataFrames must be identical for `union` to work correctly. - This method performs a simple row concatenation and does not handle any schema conflicts or mismatches. 2. `𝐮𝐧𝐢𝐨𝐧𝐁𝐲𝐍𝐚𝐦𝐞`: - The `unionByName` method is available starting from Apache Spark 3.1.0. It combines two DataFrames with potentially different schemas. - It matches the columns of both DataFrames by name and handles schema conflicts automatically. - If a column exists in both DataFrames with the same name but different data types, `unionByName` will promote the column to the wider data type to accommodate both. - If a column exists in one DataFrame but not in the other, `unionByName` will add the missing column to the resulting DataFrame. - This method provides more flexibility when dealing with DataFrames with potentially different schemas. It's worth noting that `union` and `unionByName` are used for combining DataFrames vertically (adding rows). If you need to combine DataFrames horizontally (adding columns), you can use methods like `join`, `joinWith`, or `crossJoin` depending on your specific requirements. Full Playlist of Interview Questions of SQL: ✅https://www.youtube.com/watch?v=XZHp-u5QzYE&list=PLxy0DxWEupiOU8M_GkSTYOMLB7Q9pAAZe Full Playlist of Snowflake SQL: ✅https://www.youtube.com/watch?v=no5VjmmPDGc&list=PLxy0DxWEupiP3oJralHpT8SjtdBY0BpYR Full Playlist of Golang: ✅ https://www.youtube.com/watch?v=F3Wg2dYPh_U&list=PLxy0DxWEupiMckHLAJGQuHpYkKqYhqCZ5 Full Playlist of NumPY Library: ✅ https://www.youtube.com/watch?v=F3Wg2dYPh_U&list=PLxy0DxWEupiMckHLAJGQuHpYkKqYhqCZ5 Full Playlist of PTQT5: ✅ https://www.youtube.com/watch?v=NcATA... Full Playlist of Pandas: ✅ https://www.youtube.com/watch?v=DKTcf6Wyuck&list=PLxy0DxWEupiP86ruA3QWoBG1Rb0b1BH4n YouTube Link: https://www.youtube.com/channel/UCj4qaXEHCD82DiLhKdZjX6g Instagram: https://www.instagram.com/prajaji/ pyspark tutorial azure data factory pyspark data engineer roadmap azure databricks databricks tutorial pyspark tutorial for beginners #azuredataengineer #databrickstutorial #union #databricksforbeginner #databricks #Azure #Databricks #azuredatabricks