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PySpark Fundamentals - Python for Data Engineering | Data Engineer Interview Prep | Repovive

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Data Engineer Interview Prep16 sections · 140 units8h total

Open in Course

PySpark Fundamentals

Distributed processing

PySpark distributes processing across clusters. Same logic, massive scale.

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum

spark = SparkSession.builder.appName('example').getOrCreate()

df = spark.read.parquet('s3://bucket/data/')
result = df.filter(col('status') == 'active') \
           .groupBy('region') \
           .agg(sum('revenue').alias('total_revenue'))
result.write.parquet('s3://bucket/output/')

Key concepts:

Lazy evaluation: Transformations don't execute until action called
Partitioning: Data split across workers
Caching: Keep frequently used DataFrames in memory