<ul><li>Apache Spark SQL provides various window functions like ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, CUME_DIST, PERCENT_RANK, and NTILE for data analysis purposes.</li><li>ROW_NUMBER assigns unique sequential integers to rows within partitions.</li><li>RANK assigns the same rank to rows with the same value, skipping ranks for duplicates.</li><li>DENSE_RANK, similar to RANK, assigns ranks consecutively without gaps for duplicate values.</li><li>LAG allows comparing current row's value with the previous row's value in the same result set.</li><li>LEAD enables comparisons between the current row and the next row in the result set.</li><li>CUME_DIST computes the cumulative distribution of a value in a dataset, showing its position within a group.</li><li>PERCENT_RANK returns the rank as a percentage within a partition.</li><li>NTILE divides rows in a partition into ranked groups or buckets.</li><li>These functions provide powerful analytical capabilities for Spark applications using Scala code in a local environment.</li><li>Apache Spark SQL window functions enhance data analysis possibilities, improving query performance and efficiency.</li></ul>

This One Spark SQL Trick Will Instantly Upgrade Your Data Analysis Game

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