Enterprise Data Engineering Capstone Pipeline

How do we architect a complete end-to-end PySpark pipeline combining schema enforcement, deduplication, broadcast joins, ranking, and partitioned export?

Production⏱ 4 Minutes⭐ 2000 XP

🤔 THE QUESTION

How do we architect a complete end-to-end PySpark pipeline combining schema enforcement, deduplication, broadcast joins, ranking, and partitioned export?

💡 WHAT IS IT?

The comprehensive PySpark Developer Capstone combining all core Data Engineering concepts in a single production pipeline.

🎯 WHAT IS IT USED FOR?

Enterprise-grade high-throughput data processing pipelines deployed on Databricks, EMR, and Google Cloud Dataproc.

💻 EXAMPLE

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, DoubleType, TimestampType
from pyspark.sql.window import Window
from pyspark.sql.functions import broadcast, col, count, current_timestamp, rank, round, row_number, sum, trim, upper

# 1. Initialize Distributed Session
spark = SparkSession.builder.appName("EnterpriseDataEngineeringPipeline").getOrCreate()

# 2. Strict Schema Ingestion
event_schema = StructType([
    StructField("event_id", StringType(), False),
    StructField("account_id", StringType(), False),
    StructField("amount", DoubleType(), True),
    StructField("event_time", TimestampType(), True)
])

raw_events = spark.read.schema(event_schema).parquet("s3://lakehouse/bronze/events")

# 3. Cleanse & Deduplicate
dedup_window = Window.partitionBy("event_id").orderBy(col("event_time").desc())
silver_events = raw_events \
    .filter(col("event_id").isNotNull() & (col("amount") >= 0)) \
    .withColumn("rn", row_number().over(dedup_window)) \
    .filter(col("rn") == 1) \
    .drop("rn")

# 4. Broadcast Dimension Enrichment & Gold Aggregation
dim_accounts = spark.read.parquet("s3://lakehouse/silver/dim_accounts")

gold_summary = silver_events \
    .join(broadcast(dim_accounts), "account_id", "inner") \
    .groupBy("account_id", "account_tier") \
    .agg(
        count("event_id").alias("total_txns"),
        round(sum("amount"), 2).alias("total_volume")
    ) \
    .withColumn("tier_rank", rank().over(Window.partitionBy("account_tier").orderBy(col("total_volume").desc()))) \
    .withColumn("processed_timestamp", current_timestamp())

# 5. Optimized Partitioned Storage
gold_summary.coalesce(8).write \
    .mode("overwrite") \
    .partitionBy("account_tier") \
    .option("compression", "snappy") \
    .parquet("s3://lakehouse/gold/account_kpis")

🎯 Mission Objectives

Practice typing production-grade PySpark code for Enterprise Data Engineering Capstone Pipeline.

✓ Initialize enterprise SparkSession
✓ Enforce strict StructType schema on Bronze ingestion
✓ Deduplicate, enrich with broadcast join, and export partitioned Gold KPI mart

🚀 Start Mission

← Prev (Scalable Bronze-to-Gold Warehouse Transformation)Next Lesson (Complex Multi-Tier when/otherwise Logic) →