df = spark.read.parquet("sales.parquet") df.filter("amount > 1000").groupBy("region").count().show() You can register DataFrames as temporary views and run SQL:
from pyspark.sql import SparkSession spark = SparkSession.builder .appName("MyApp") .config("spark.sql.adaptive.enabled", "true") .getOrCreate() 3.1 RDD – The Original Foundation RDDs (Resilient Distributed Datasets) are low‑level, immutable, partitioned collections. They provide fault tolerance via lineage. However, they are not recommended for new projects because they lack optimization. beginning apache spark 3 pdf
squared_udf = udf(squared, IntegerType()) df.withColumn("squared_val", squared_udf(df.value)) df = spark
query.awaitTermination() Structured Streaming uses checkpointing and write‑ahead logs to guarantee end‑to‑end exactly‑once processing. 6.4 Event Time and Watermarks Handle late data efficiently: beginning apache spark 3 pdf