PySpark in 7 Days (Day 7)

Learn PySpark in 7 Days – Day 7

Welcome to Day 7 — the most important day of this series. Today, we bring everything together and build a real-world, end-to-end PySpark ETL pipeline using best practices expected from a professional data engineer.

Day 7 Focus:
• End-to-end PySpark ETL pipeline
• Bronze → Silver → Gold architecture
• Performance-aware transformations
• Interview-ready explanation


Real-World Scenario

We receive daily employee data as CSV files. Our task is to:

  • Ingest raw data (Bronze)
  • Clean and transform data (Silver)
  • Create analytics-ready output (Gold)

Step 1: Create Spark Session

from pyspark.sql import SparkSession spark = SparkSession.builder \ .appName("EndToEnd-PySpark-Pipeline") \ .getOrCreate()

Step 2: Bronze Layer – Read Raw Data

bronze_df = spark.read \ .option("header", "true") \ .option("inferSchema", "true") \ .csv("/data/bronze/employees.csv") bronze_df.show()
Bronze layer stores data as received, with minimal transformation.

Step 3: Silver Layer – Clean & Transform

from pyspark.sql.functions import col silver_df = bronze_df \ .filter(col("salary").isNotNull()) \ .withColumn("salary", col("salary").cast("int")) \ .withColumnRenamed("dept", "department") silver_df.show()
  • Removed invalid records
  • Standardised column names
  • Applied data types

Step 4: Enrichment (Join with Lookup)

departments = [ (10, "IT"), (20, "HR"), (30, "Finance") ] dept_df = spark.createDataFrame( departments, ["dept_id", "dept_name"] ) silver_enriched_df = silver_df.join( dept_df, silver_df.dept_id == dept_df.dept_id, "left" )

Step 5: Gold Layer – Aggregations

from pyspark.sql.functions import avg, count gold_df = silver_enriched_df.groupBy("dept_name").agg( count("*").alias("employee_count"), avg("salary").alias("avg_salary") ) gold_df.show()
Gold layer is business-ready and optimised for reporting.

Step 6: Write Output (Parquet)

gold_df.coalesce(1).write \ .mode("overwrite") \ .parquet("/data/gold/employee_metrics")
  • Parquet for performance
  • coalesce to control file count

How to Explain This in an Interview

Sample Answer:

“I designed an end-to-end PySpark pipeline using a Bronze–Silver–Gold approach. Raw CSV data was ingested into the Bronze layer with minimal changes. In the Silver layer, I applied schema enforcement, filtering, and enrichment joins. Finally, in the Gold layer, I aggregated the data into analytics-ready metrics and stored it in Parquet for efficient downstream consumption.”

Key Concepts You Mastered in 7 Days

  • PySpark DataFrames and Spark SQL
  • Joins, aggregations, and window functions
  • Performance tuning and partitioning
  • End-to-end ETL pipeline design

What’s Next?

  • Practice with real datasets
  • Learn Delta Lake & MERGE patterns
  • Run pipelines on Databricks / Fabric
  • Prepare PySpark interview questions
Series: Learn PySpark in 7 Days
Author: TrySpark
Outcome: Production-ready PySpark foundation

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