**Code**:
```python
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("csv_load").getOrCreate()
df = spark.read.option("header", True).option("inferSchema", True).csv("/path/file.csv")
df.write.saveAsTable("my_table") # or createOrReplaceTempView
```
**Production**: Specify schema (no inferSchema). Partitioning if large. Explicit mode (overwrite/append).
**Why Schema**: inferSchema scans data; slow and can be wrong....
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 HCL. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.