Skip to main content

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
Labels:

Comments

Post a Comment

Popular posts from this blog

Exploring the Largest UK Employers: A Power BI Visualization

Understanding employment distribution among top companies can provide valuable insights into industry dominance and workforce trends. In this blog, I analyze the largest employers in the UK using a Power BI table visualization, sourced from CompaniesMarketCap . Source:  CompaniesMarketCap Key Insights from the Data: Compass Group leads the ranking with 550,000 employees, dominating the food service industry. Tesco, the retail giant, follows with 330,000 employees. HSBC, a major player in banking, employs over 215,000 people. The total workforce among the top companies surpasses 1.98 million employees. Visualizing in Power BI: Using a table visualization, we can clearly compare the number of employees across different companies. Power BI’s sorting, aggregation, and filtering features enhance data readability and analysis. However, incorporating bar charts, conditional formatting, and KPIs could make the insights even more compelling. What’s Next? Would you add more interactive eleme...
Post a Comment
Read more

Master Databricks Asset Bundles Through Hands-On Practice

15 min read | 100% Practical Guide Forget theory. Forget abstract examples. This is a hands-on, build-as-you-learn guide to mastering YAML through the lens of Databricks Asset Bundles (DABs) . By the end of this post, you'll go from never writing YAML to confidently deploying production-grade data pipelines as code. 🎯 What You'll Build: A complete Databricks workspace configuration including jobs, clusters, notebooks, and permissions—all defined in YAML and deployable with a single command. Level 0: YAML Basics BEGINNER The Golden Rules Rule #1: YAML uses spaces for indentation , never tabs. Standard is 2 spaces per level. Rule #2: YAML is case-sensitive . Name ≠ name Rule #3: Indentation = Structure . It defines parent-child relationships. ...
Post a Comment
Read more

PySpark Important Last-Minute Notes

If you are preparing for Data Engineering interviews , Spark projects , or need a quick PySpark revision , this post consolidates the most important PySpark concepts in one place. Best for: Data Engineers, Big Data Developers, Azure/Databricks/Microsoft Fabric users, and anyone doing last-minute interview preparation. What is PySpark? PySpark is the Python API for Apache Spark , an open-source distributed computing framework used for large-scale data processing. Why PySpark? Distributed, in-memory processing Faster than traditional batch systems Scales across clusters Supports SQL, Streaming, and Machine Learning Common use cases ETL / ELT pipelines Big data analytics Machine learning at scale Real-time and batch processing Spark Cluster Architecture A Spark cluster typically consists of: Master Node: Manages resources and schedules tasks Worker Nodes: Execute tasks in parallel This architecture enables efficient distributed p...
Post a Comment
Read more