Sports Data Analysis using PySpark - Part 02

Dive into the world of big data processing with our PySpark Practice playlist. This series is designed for both beginners and seasoned data professionals looking to sharpen their Apache Spark skills through scenario-based questions and challenges.
Each video provides step-by-step solutions to real-world problems, helping you master PySpark techniques and improve your data-handling capabilities. Whether preparing for a job interview or just learning more about Spark, this playlist is your go-to resource for practical, hands-on learning. Join us to become a PySpark expert!
This playlist will focus on the practical aspect of learning PySpark by hands-on coding firstly using SQL to solve the problem & then solving the same problem using PySpark.
While solving the problems, we are also demonstrating the most asked PySpark #interview problems.
Stay tuned to all to this playlist for all upcoming videos.
𝗝𝗼𝗶𝗻 𝗺𝗲 𝗼𝗻 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮:
🔅 Topmate (For collaboration and Scheduling calls) - topmate.io/ankur_ranjan
🔅 LinkedIn - / thebigdatashow
🔅 Instagram - / ranjan_anku
#pyspark #practice #dataengineering #apachespark #problemsolving #spark #bigdata

[@maazahmedansari4334]

Insightful, thank you for uploading.
We can avoid min window function in last problem.
Solution:
windowSpec = Window.partitionBy("user_id").orderBy("login_date")
lead_df = input_df.withColumn(
"next_date",
f.lead("login_date").over(windowSpec)
).orderBy("user_id", "login_date")
result = lead_df.select("user_id").filter(
f.date_diff(f.col("next_date"), f.col("login_date")) == 1
)
result.show()

[@siddheshchavan2069]

Great series, can you upload more such videos with complex problems and bigger datasets

[@TheBigDataShow]

Please check the other video from the same playlist. We have uploaded near to 4 videos