M𝗼𝘀𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘂𝘀𝗲 #𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘆… 𝗯𝘂𝘁 𝗳𝗲𝘄 𝗸𝗻𝗼𝘄 𝘄𝗵𝗶𝗰𝗵 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝘅𝗶𝗺𝗶𝘇𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲.

Ever written long UDFs, confusing joins, or bulky transformations?
Most of that effort is unnecessary — #Spark already gives you built-ins for almost everything.

𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 (𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐏𝐃𝐅)
• Core Ops: select(), withColumn(), filter(), dropDuplicates()
• Aggregations: groupBy(), countDistinct(), collect_list()
• Strings: concat(), split(), regexp_extract(), trim()
• Window: row_number(), rank(), lead(), lag()
• Date/Time: current_date(), date_add(), last_day(), months_between()
• Arrays/Maps: array(), array_union(), MapType

Just mastering these ~20 functions can simplify 70% of your transformations.