Sports Data Analysis using PySpark - Part 01

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.
Use the below input code to generate the input PySpark #dataframe & use Google Colab Notebook to write & practice PySpark Code.
```
Install PySpark
!pip install pyspark
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, IntegerType, StringType
from pyspark.sql import functions as f
from pyspark.sql.window import Window
Create a Spark session
spark = (
SparkSession.builder
.appName("thebigdatashow.me")
.getOrCreate()
)
Define the schema corresponding to the data
schema = StructType([
StructField("user_id", IntegerType(), True),
StructField("kit_id", IntegerType(), True),
StructField("login_date", StringType(), True),
StructField("sessions_count", IntegerType(), True)
])
Data to be loaded into DataFrame
data = [
(1, 2, "2016-03-01", 5),
(1, 2, "2016-03-02", 6),
(2, 3, "2017-06-25", 1),
(3, 1, "2016-03-02", 0),
(3, 4, "2018-07-03", 5)
]
Create DataFrame
inputDF = spark.createDataFrame(data, schema=schema)
Show the DataFrame
inputDF.show()
```
Stay tuned to all to this playlist for all upcoming videos.
Upcoming Part 2 of the video - • Sports Data Analysis u...
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