**Why It Matters (Architectural Logic)**: A complete program demonstrates lifecycle management—getOrCreate, stop, config. Production requires try/finally and checkpoint for long lineages.
A complete PySpark program structure:
```python
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
spark = SparkSession.builder.appName("MyJob").getOrCreate()
df = spark.read.parquet("s3://input/")
transformed = df.withColumn("new_col", F.upper(F.col("name"))).filter(F.col("active") =...
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Carelon. The answer also includes follow-up discussion points that interviewers commonly explore.
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