🚀 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗦𝗽𝗮𝗿𝗸 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀: 𝗡𝗮𝗿𝗿𝗼𝘄 𝘃𝘀. 𝗪𝗶𝗱𝗲 🚀 📌In Apache Spark, the difference between narrow and wide transformations is crucial for optimizing performance. 🔹 𝗡𝗮𝗿𝗿𝗼𝘄 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀: 👉🏻Includes operations like map, flatMap, filter, union, coalesce, and repartition. 👉🏻Features a one-to-one mapping between input partitions and file blocks. 👉🏻Executes within a single stage without requiring data shuffling or movement across partitions. 👉🏻Each input partition contributes to only one output partition, making it more efficient. 👉🏻Mapping between file blocks and RDD partitions is handled internally for narrow transformations, 🔹 𝗪𝗶𝗱𝗲 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀: 👉🏻Involves operations like groupByKey(), reduceByKey(), join(), cogroup(), and distinct(). 👉🏻Allows each input partition to contribute to multiple output partitions. 👉🏻Requires data shuffling and movement across partitions, often ...