Notebooks — cdc_streaming-online_retail-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._
import org.apache.spark.sql.types._
import org.apache.spark.sql.DataFrame

PySpark (Jupyter):

from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from pyspark.sql.types import DoubleType, IntegerType, StringType, StructType

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

2.2 Read¶

Scala (Zeppelin):

spark.sql("CREATE TABLE IF NOT EXISTS lakehouse.silver.online_retail_cdc (invoice string, stock_code string, quantity int, price double) USING iceberg")
val schema = new StructType().add("invoice", StringType).add("stock_code", StringType).add("quantity", IntegerType).add("price", DoubleType)
val raw = spark.readStream.format("kafka").option("kafka.bootstrap.servers", "redpanda:9092").option("subscribe", "online_retail_cdc").option("startingOffsets", "earliest").load()
val cdc = raw.select(from_json($"value".cast("string"), schema).as("c")).select("c.*")

PySpark (Jupyter):

spark.sql("CREATE TABLE IF NOT EXISTS lakehouse.silver.online_retail_cdc (invoice string, stock_code string, quantity int, price double) USING iceberg")
schema = StructType().add("invoice", StringType()).add("stock_code", StringType()).add("quantity", IntegerType()).add("price", DoubleType())
raw = spark.readStream.format("kafka").option("kafka.bootstrap.servers", "redpanda:9092").option("subscribe", "online_retail_cdc").option("startingOffsets", "earliest").load()
cdc = raw.select(F.from_json(F.col("value").cast("string"), schema).alias("c")).select("c.*")

2.3 Transform¶

Scala (Zeppelin):

val parsed = cdc
// CDC rows are upserted per micro-batch in the Write step

PySpark (Jupyter):

parsed = cdc
# CDC rows are upserted per micro-batch in the Write step

2.4 Write¶

Scala (Zeppelin):

val query = parsed.writeStream.foreachBatch { (batchDF: DataFrame, batchId: Long) =>
  batchDF.createOrReplaceTempView("cdc_batch")
  batchDF.sparkSession.sql("MERGE INTO lakehouse.silver.online_retail_cdc t USING cdc_batch s ON t.invoice = s.invoice AND t.stock_code = s.stock_code WHEN MATCHED THEN UPDATE SET t.quantity = s.quantity, t.price = s.price WHEN NOT MATCHED THEN INSERT *")
}.option("checkpointLocation", "s3a://checkpoints/online_retail_cdc").start()

PySpark (Jupyter):

def upsert_batch(batch_df, batch_id):
    batch_df.createOrReplaceTempView("cdc_batch")
    batch_df.sparkSession.sql("MERGE INTO lakehouse.silver.online_retail_cdc t USING cdc_batch s ON t.invoice = s.invoice AND t.stock_code = s.stock_code WHEN MATCHED THEN UPDATE SET t.quantity = s.quantity, t.price = s.price WHEN NOT MATCHED THEN INSERT *")

query = parsed.writeStream.foreachBatch(upsert_batch).option("checkpointLocation", "s3a://checkpoints/online_retail_cdc").start()

2.5 Verify¶

Scala (Zeppelin):

spark.table("lakehouse.silver.online_retail_cdc").orderBy("invoice").show(false)

PySpark (Jupyter):

spark.table("lakehouse.silver.online_retail_cdc").orderBy("invoice").show(truncate=False)

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.