Table API & PyFlink — one pipeline, two languages

4 h
requires FLK-04

From your world. a dbt model expressed as a relational pipeline, written once and runnable from Java or Python (like dbt SQL vs dbt Python models)

Practice quiz with instant feedback. For a scored run that counts toward your local progress, use uv run de quiz FLK-08.

2You write a pipeline once in the Java Table API and once in the PyFlink Table API. Both run over the same data. What must be true? AThey may differ because Python rounds differently BThey produce the same result. PyFlink builds the same logical plan and runs on the same JVM engine COnly the Java one is correct DPyFlink runs a separate Python engine with different results

6Why can a UDF-heavy PyFlink job be slower than the Java equivalent? APython is always slower for everything BEach record crosses the Python/JVM boundary, and this is costly. If you keep the work in SQL/Table API (JVM) or use vectorized UDFs, you avoid per-row round-trips CPyFlink does not use checkpoints DPyFlink cannot aggregate

8Which are valid ways to write the same per-currency GMV aggregation in Flink? (choose all) (select all that apply) AFlink SQL: SELECT ccy, SUM(amt) FROM orders GROUP BY ccy BJava Table API: tbl.groupBy($("ccy")).select($("ccy"), $("amt").sum()) CPyFlink Table API: tbl.group_by(col("ccy")).select(col("ccy"), col("amt").sum) DA DataStream keyBy(ccy).sum on the amount field