Notebooks — star_schema-tpch-spark-iceberg

Auto-extracted from jupyter/notebook.ipynb and zeppelin/notebook.zpln. Both notebooks implement identical logic in PySpark and Scala.

1. Section map

Subsection	Scala (Zeppelin)	PySpark (Jupyter)
2.1 Setup	✓	✓
2.2 Read	✓	✓
2.3 Transform	✓	✓
2.4 Write	✓	✓
2.5 Verify	✓	✓

2. Walkthrough

2.1 Setup

Scala (Zeppelin):

import spark.implicits._
import org.apache.spark.sql.functions._
// spark pre-bound (Spark Connect + lakehouse catalog)

PySpark (Jupyter):

from pyspark.sql import SparkSession
from pyspark.sql import functions as F

spark = SparkSession.builder.remote("sc://spark-connect:15002").getOrCreate()

2.2 Read

Scala (Zeppelin):

val orders = spark.read.parquet("s3a://landing/tpch/orders")
val customer = spark.read.parquet("s3a://landing/tpch/customer")
val lineitem = spark.read.parquet("s3a://landing/tpch/lineitem")

PySpark (Jupyter):

orders = spark.read.parquet("s3a://landing/tpch/orders")
customer = spark.read.parquet("s3a://landing/tpch/customer")
lineitem = spark.read.parquet("s3a://landing/tpch/lineitem")

2.3 Transform

Scala (Zeppelin):

val dimCustomer = customer.select($"c_custkey", $"c_name", $"c_nationkey", $"c_mktsegment")
val fctOrders = orders.join(lineitem, orders("o_orderkey") === lineitem("l_orderkey"))
  .groupBy($"o_orderkey", $"o_custkey", $"o_orderdate")
  .agg(sum($"l_extendedprice").as("revenue"), count("*").as("line_count"))

PySpark (Jupyter):

dimCustomer = customer.select(F.col("c_custkey"), F.col("c_name"), F.col("c_nationkey"), F.col("c_mktsegment"))
fctOrders = (orders.join(lineitem, orders["o_orderkey"] == lineitem["l_orderkey"])
    .groupBy(F.col("o_orderkey"), F.col("o_custkey"), F.col("o_orderdate"))
    .agg(F.sum("l_extendedprice").alias("revenue"), F.count("*").alias("line_count")))

2.4 Write

Scala (Zeppelin):

dimCustomer.writeTo("lakehouse.gold.dim_customer").using("iceberg").createOrReplace()
fctOrders.writeTo("lakehouse.gold.fct_orders").using("iceberg").createOrReplace()

PySpark (Jupyter):

dimCustomer.writeTo("lakehouse.gold.dim_customer").using("iceberg").createOrReplace()
fctOrders.writeTo("lakehouse.gold.fct_orders").using("iceberg").createOrReplace()

2.5 Verify

Scala (Zeppelin):

spark.sql("SELECT c.c_mktsegment, sum(f.revenue) AS revenue FROM lakehouse.gold.fct_orders f JOIN lakehouse.gold.dim_customer c ON f.o_custkey = c.c_custkey GROUP BY c.c_mktsegment").show()

PySpark (Jupyter):

spark.sql("SELECT c.c_mktsegment, sum(f.revenue) AS revenue FROM lakehouse.gold.fct_orders f JOIN lakehouse.gold.dim_customer c ON f.o_custkey = c.c_custkey GROUP BY c.c_mktsegment").show()

3. Scala / PySpark parity

Both notebooks share the same numbered sections and produce identical Iceberg tables; only the language and interpreter differ.

4. How to run

Open the scenario's zeppelin/notebook.zpln on the Atlas Zeppelin UI or jupyter/notebook.ipynb on JupyterHub, then run all paragraphs/cells top to bottom.