In this project, you will build a complete distributed data processing pipeline using PySpark and Apache Spark.<\/p>\n\n\n\n
You will process large sales data, clean it, transform it, and generate business insights just like a real Data Engineer.<\/p>\n\n\n\n
A retail company has:<\/p>\n\n\n\n
Your task is to build a scalable distributed processing pipeline.<\/p>\n\n\n\n
Raw CSV Files \u2192 PySpark Processing \u2192 Aggregation \u2192 Save as Parquet \u2192 Dashboard\/BI Tool<\/p>\n\n\n\n
from pyspark.sql import SparkSessionspark = SparkSession.builder \\
.appName(\"Distributed Data Processing Project\") \\
.getOrCreate()<\/pre>\n\n\n\nStep 2: Load Large Dataset<\/h2>\n\n\n\n
df = spark.read.csv(\"sales_data.csv\",
header=True,
inferSchema=True)df.show(5)
df.printSchema()<\/pre>\n\n\n\nStep 3: Data Cleaning<\/h2>\n\n\n\n
Remove null values:<\/p>\n\n\n\n
df = df.dropna()<\/pre>\n\n\n\nRemove duplicates:<\/p>\n\n\n\n
df = df.dropDuplicates()<\/pre>\n\n\n\nFilter invalid sales:<\/p>\n\n\n\n
df = df.filter(df.amount > 0)<\/pre>\n\n\n\nStep 4: Data Transformation<\/h2>\n\n\n\n
Create a new column:<\/p>\n\n\n\n
from pyspark.sql.functions import coldf = df.withColumn(\"total_price\", col(\"quantity\") * col(\"amount\"))<\/pre>\n\n\n\nConvert date column:<\/p>\n\n\n\n
from pyspark.sql.functions import to_datedf = df.withColumn(\"sale_date\", to_date(\"sale_date\", \"yyyy-MM-dd\"))<\/pre>\n\n\n\nStep 5: Aggregation<\/h2>\n\n\n\n
Total revenue by region:<\/p>\n\n\n\n
revenue_by_region = df.groupBy(\"region\") \\
.sum(\"total_price\")revenue_by_region.show()<\/pre>\n\n\n\nTop-selling products:<\/p>\n\n\n\n
top_products = df.groupBy(\"product\") \\
.sum(\"quantity\") \\
.orderBy(\"sum(quantity)\", ascending=False)top_products.show()<\/pre>\n\n\n\nStep 6: Optimization Techniques<\/h2>\n\n\n\n
Repartition data:<\/p>\n\n\n\n
df = df.repartition(4)<\/pre>\n\n\n\nCache frequently used data:<\/p>\n\n\n\n
df.cache()<\/pre>\n\n\n\nStep 7: Save Processed Data<\/h2>\n\n\n\n
Save as Parquet:<\/p>\n\n\n\n
revenue_by_region.write.parquet(\"output\/revenue_by_region\")<\/pre>\n\n\n\nSave as CSV:<\/p>\n\n\n\n
revenue_by_region.write.csv(\"output\/revenue_csv\", header=True)<\/pre>\n\n\n\nStep 8: Real-World Extensions<\/h2>\n\n\n\n
You can enhance this project by:<\/p>\n\n\n\n
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- Connecting to MySQL or PostgreSQL<\/li>\n\n\n\n
- Reading data from APIs<\/li>\n\n\n\n
- Scheduling jobs with Airflow<\/li>\n\n\n\n
- Processing streaming data<\/li>\n\n\n\n
- Creating dashboards in Power BI<\/li>\n<\/ul>\n\n\n\n
Skills You Practice<\/h2>\n\n\n\n
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- Reading large datasets<\/li>\n\n\n\n
- Distributed processing<\/li>\n\n\n\n
- Data cleaning<\/li>\n\n\n\n
- Transformations and aggregations<\/li>\n\n\n\n
- Performance optimization<\/li>\n\n\n\n
- Writing optimized output formats<\/li>\n<\/ul>\n\n\n\n
Interview-Level Explanation<\/h2>\n\n\n\n
If asked in an interview:<\/p>\n\n\n\n
“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.”<\/p>\n\n\n\n
Final Outcome<\/h2>\n\n\n\n
By completing this project, you understand:<\/p>\n\n\n\n
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- How distributed systems process large data<\/li>\n\n\n\n
- How Spark handles transformations and actions<\/li>\n\n\n\n
- How to build scalable Big Data pipelines<\/li>\n<\/ul>\n\n\n\n
This is a complete beginner-to-intermediate level Data Engineering project using PySpark.<\/p>\n\n\n