IPL Data Project with Apache Spark and Databricks

Project Overview

IPL Dataset: Indian cricket Premier League data
Tools Used:
- Amazon S3: Data storage
- Databricks: Environment and tools
- Apache Spark: Transformation and analytics
- SQL: Data analysis
- Visualization: Data visualization to understand insights

Project Steps

Upload Data: Upload IPL dataset to Amazon S3
Databricks Environment: Use Databricks to create and manage Spark operations
Apache Spark: Write PySpark code for transformations
SQL Analysis: Write SQL queries to analyze data
Visualization: Build visual representations of the data

Architecture Diagram

Basic architecture of project execution

Introduction to Apache Spark

Spark Core: Heart of Spark for executing code
Spark SQL: Write SQL queries within Spark applications
Spark Streaming: Process real-time data
Spark MLlib: Machine learning library in Spark
GraphX: Processing graphical data
Spark API Levels: RDDs, DataFrames, Datasets
Cluster Management: Execution of Spark applications across clusters
Driver and Executor: Core components of Spark
- Driver manages and schedules tasks
- Executors run the scheduled tasks
Spark Session: Entry point for Spark applications
Lazy Evaluation: Transformations applied on data when an action is called

Apache Spark Transformations

Schema Definition: Define the structure of the dataset using StrucField and StructType
Basic Transformations: Filtering, Mapping, Aggregations
Window Functions: Compute metrics over specified windows
Actions: Trigger execution of transformations

Databricks Environment

How to set up Databricks Community Edition
Creating a compute cluster
Creating and managing notebooks

Reading Data with PySpark

Read data from S3 bucket into Spark DataFrames
Define schema to ensure correct data types
Code sample for reading CSV files and defining schema

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, BooleanType, DateType

spark = SparkSession.builder.appName('IPLDataAnalysis').getOrCreate()

schema = StructType([
    StructField('match_id', IntegerType(), True),
    StructField('over_id', IntegerType(), True),
    StructField('ball_id', IntegerType(), True),
    StructField('innings_number', IntegerType(), True),
    StructField('team_batting', StringType(), True),
    StructField('team_bowling', StringType(), True)
    #... additional fields
])

ball_by_ball_df = spark.read.format('csv').schema(schema).option('header', 'true').load('s3a://bucket/path/to/csv')

Writing Transformations

Example transformations: Filtering wides and no-balls, aggregating runs
Example of window function: Running total of runs in a match for each over

SQL Analytics

Convert DataFrames to SQL Temp Views

ball_by_ball_df.createOrReplaceTempView('ball_by_ball')

Example SQL queries: Top scoring batsmen per season, economical bowlers in power-play

Visualization

Convert results to Pandas DataFrames
Visualization libraries: Matplotlib, Seaborn
Example visualizations: Top scoring batsmen, toss impact, average runs

Additional Notes

Utilize ChatGPT for generating code snippets to increase productivity
Practice writing PySpark code to gain confidence
Focus on project-specific transformations and visualizations as per insights required

Conclusion

This project shows the end-to-end data processing pipeline using Apache Spark and Databricks
Key learning goals: Spark transformations, SQL analytics, data visualization
Recommended further learning: Apache Spark in-depth courses for more advanced topics and hands-on projects