Notebooks — json_flatten-gh_archive-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):
PySpark (Jupyter):
2.3 Transform¶
Scala (Zeppelin):
val flat = raw.select($"id", $"type", $"actor.login".as("actor_login"), $"repo.name".as("repo_name"), $"created_at".cast("timestamp").as("created_at"))
PySpark (Jupyter):
flat = raw.select(
F.col("id"),
F.col("type"),
F.col("actor.login").alias("actor_login"),
F.col("repo.name").alias("repo_name"),
F.col("created_at").cast("timestamp").alias("created_at")
)
2.4 Write¶
Scala (Zeppelin):
PySpark (Jupyter):
2.5 Verify¶
Scala (Zeppelin):
spark.sql("SELECT type, count(*) AS n FROM lakehouse.silver.gh_events GROUP BY type ORDER BY n DESC").show()
PySpark (Jupyter):
spark.sql("SELECT type, count(*) AS n FROM lakehouse.silver.gh_events GROUP BY type ORDER BY n DESC").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.