6. Notebooks¶
Each scenario ships paired Zeppelin (Scala) and Jupyter (PySpark) notebooks with identical logic. A comprehensive, auto-extracted notebook doc per scenario lives below — also reachable from each scenario's §4.
The docs below are generated from scenarios/<name>/jupyter/notebook.ipynb and scenarios/<name>/zeppelin/notebook.zpln by scripts/docslib/notebooks.py. Each doc shows the section map, the side-by-side Scala/PySpark walkthrough for every numbered section, and the Scala/PySpark parity statement.
Batch¶
Streaming¶
- streaming_ingest-events-spark-iceberg
- streaming_ingest-gh_archive-spark-iceberg
- streaming_windows-events-spark-iceberg
- cdc_streaming-online_retail-spark-iceberg
Quality / Modeling¶
- data_quality-nyc_taxi-spark-iceberg
- schema_evolution-gh_archive-spark-iceberg
- star_schema-tpch-spark-iceberg
- feature_engineering-movielens-spark-iceberg
- scd2-online_retail-spark-iceberg
Ops¶
- time_travel-nyc_taxi-spark-iceberg
- table_maintenance-nyc_taxi-spark-iceberg
- incremental_upsert-online_retail-spark-iceberg
SQL / Analytics¶
- bi_query-tpch-trino-iceberg
- federated_query-nyc_taxi-trino-iceberg
- join_optimization-tpch-spark-iceberg