**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Check Spark version: In PySpark: `spark.version` or `spark.sparkContext.version`. Scala: `org.apache.spark.SparkContext.SPARK_VERSION`. Shell: `spark-submit --version` or `pyspark --version`. Databricks: Runtime version in cluster config (e.g., 13.3 LTS = Spark 3.4)....
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 Deloitte. The answer also includes follow-up discussion points that interviewers commonly explore.
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