PySpark Window Functions Tutorial
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PySpark Window Functions Tutorial: ROW_NUMBER, LEAD & LAG on Time-Ordered Data
In this video, I demonstrate how to use PySpark window functions to analyze trends and relationships in time-ordered datasets. Using a real-world CSV file, we walk through building a Spark DataFrame, partitioning it by country, and ordering it by date_added to perform insightful row-level comparisons.
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What’s Covered in This Video:
1. Spark Environment Setup:
• Import key PySpark classes: SparkSession, Window, row_number, lead, lag.
• Launch a local Spark session for processing.
2. Loading Data into Spark:
• Read a CSV file with headers and inferred schema.
• Print and verify the DataFrame schema.
3. Window Specification:
• Define a window partitioned by country and ordered by date_added.
• This structure powers all subsequent row-wise computations within each group.
4. Applying Core Window Functions:
• Use ROW_NUMBER() to assign a unique rank to each row per country group.
• Use LEAD() and LAG() to peek at next and previous rows for trend analysis and comparisons.
5. Result Inspection:
• Preview the updated DataFrame after each transformation.
• Validate that the new window-based columns behave as expected.
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Why Watch This Video?
• Learn how to rank, compare, and analyze data trends within grouped partitions.
• Understand when and how to use lead and lag to explore sequential row relationships.
• Gain hands-on experience with time-series-like window operations in PySpark.
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Who Is This Video For?
• Data engineers and analysts working with time-ordered and grouped datasets.
• PySpark beginners who want to master practical windowing techniques.
• Anyone building analytical pipelines using Spark DataFrames.
Dataset Link:
https://github.com/raghuveertechzone/...
Time Stamps:
00:00 Intro
Pyspark for beginners
Pyspark Tutorial
Big Data Tutorial
Big Data with Pyspark
Big Data solutions
Pyspark Full Tutorial
Pyspark window functions
Distributed Data Processing with Pyspark
credits:Pulkit Chaddha