Distributed Data Processing Project

Project Overview

In this project, you will build a complete distributed data processing pipeline using PySpark and Apache Spark.

You will process large sales data, clean it, transform it, and generate business insights just like a real Data Engineer.

Project Scenario

A retail company has:

• Large CSV sales files (millions of records)
• Data from multiple regions
• Product and customer information
• Need for daily revenue reports

Your task is to build a scalable distributed processing pipeline.

Project Architecture

Raw CSV Files → PySpark Processing → Aggregation → Save as Parquet → Dashboard/BI Tool

Step 1: Setup Spark Session

from pyspark.sql import SparkSessionspark = SparkSession.builder \
    .appName("Distributed Data Processing Project") \
    .getOrCreate()

Step 2: Load Large Dataset

df = spark.read.csv("sales_data.csv", 
                    header=True, 
                    inferSchema=True)df.show(5)
df.printSchema()

Step 3: Data Cleaning

Remove null values:

df = df.dropna()

Remove duplicates:

df = df.dropDuplicates()

Filter invalid sales:

df = df.filter(df.amount > 0)

Step 4: Data Transformation

Create a new column:

from pyspark.sql.functions import coldf = df.withColumn("total_price", col("quantity") * col("amount"))

Convert date column:

from pyspark.sql.functions import to_datedf = df.withColumn("sale_date", to_date("sale_date", "yyyy-MM-dd"))

Step 5: Aggregation

Total revenue by region:

revenue_by_region = df.groupBy("region") \
                      .sum("total_price")revenue_by_region.show()

Top-selling products:

top_products = df.groupBy("product") \
                 .sum("quantity") \
                 .orderBy("sum(quantity)", ascending=False)top_products.show()

Step 6: Optimization Techniques

Repartition data:

df = df.repartition(4)

Cache frequently used data:

df.cache()

Step 7: Save Processed Data

Save as Parquet:

revenue_by_region.write.parquet("output/revenue_by_region")

Save as CSV:

revenue_by_region.write.csv("output/revenue_csv", header=True)

Step 8: Real-World Extensions

You can enhance this project by:

• Connecting to MySQL or PostgreSQL
• Reading data from APIs
• Scheduling jobs with Airflow
• Processing streaming data
• Creating dashboards in Power BI

Skills You Practice

• Reading large datasets
• Distributed processing
• Data cleaning
• Transformations and aggregations
• Performance optimization
• Writing optimized output formats

Interview-Level Explanation

If asked in an interview:

“I built a distributed data processing pipeline using PySpark. The system loads large CSV files, performs cleaning and transformations, calculates aggregated revenue metrics, and stores optimized Parquet files for downstream analytics.”

Final Outcome

By completing this project, you understand:

• How distributed systems process large data
• How Spark handles transformations and actions
• How to build scalable Big Data pipelines

This is a complete beginner-to-intermediate level Data Engineering project using PySpark.