6.16. bi_query-tpch-trino-iceberg¶

Documents the scenario's paired Jupyter (notebook.ipynb) and Zeppelin (notebook.zpln) implementations. Both notebooks implement the same Trino queries through different clients.

1. Section map¶

Subsection	Trino SQL (Zeppelin)	Python client (Jupyter)
2.1 Setup	✓	✓
2.2 Read	✓	✓
2.3 Transform	✓	✓
2.4 Write	✓	✓
2.5 Verify	✓	✓

2. Walkthrough¶

2.1 Setup¶

Trino SQL (Zeppelin):

-- %trino is pre-bound to the Atlas Trino coordinator (catalog: lakehouse)

Python client (Jupyter):

from trino.dbapi import connect

cur = connect(host='trino', port=8080, user='atlas', catalog='lakehouse').cursor()
def q(sql):
    cur.execute(sql)
    return cur.fetchall()

2.2 Read¶

Trino SQL (Zeppelin):

SELECT * FROM lakehouse.gold.fct_orders LIMIT 10

Python client (Jupyter):

q('SELECT * FROM lakehouse.gold.fct_orders LIMIT 10')

2.3 Transform¶

Trino SQL (Zeppelin):

SELECT c.c_mktsegment, sum(f.revenue) AS revenue, sum(f.line_count) AS lines
FROM lakehouse.gold.fct_orders f
JOIN lakehouse.gold.dim_customer c ON f.o_custkey = c.c_custkey
GROUP BY c.c_mktsegment ORDER BY revenue DESC

Python client (Jupyter):

q('SELECT c.c_mktsegment, sum(f.revenue) AS revenue, sum(f.line_count) AS lines '
  'FROM lakehouse.gold.fct_orders f '
  'JOIN lakehouse.gold.dim_customer c ON f.o_custkey = c.c_custkey '
  'GROUP BY c.c_mktsegment ORDER BY revenue DESC')

2.4 Write¶

Trino SQL (Zeppelin):

CREATE TABLE IF NOT EXISTS lakehouse.gold.bi_segment_revenue AS
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

Python client (Jupyter):

q('CREATE TABLE IF NOT EXISTS lakehouse.gold.bi_segment_revenue AS '
  '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')

2.5 Verify¶

Trino SQL (Zeppelin):

SELECT count(*) FROM lakehouse.gold.bi_segment_revenue

Python client (Jupyter):

q('SELECT count(*) FROM lakehouse.gold.bi_segment_revenue')

3. Trino query equivalence¶

Both notebooks share the same numbered sections and issue equivalent Trino SQL. The Zeppelin notebook uses %trino; Jupyter uses the Python DB-API client. These two scenarios are execution-gated but are not included in the 17 Scala/PySpark parity pairs.

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.