PySpark

Spark Architecture: Understanding Driver, Executors, Jobs, Stages, and Tasks

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Data Engineering

Apache Spark is a distributed data processing engine that simplifies executing large-scale data pipelines through an organized structure. This structure includes a Driver for coordination, Cluster Manager for resource allocation, and Executors for data processing. Understanding the hierarchy of Application, Job, Stage, and Task is crucial for effective use of Spark.

Pyspark Questions and Answers

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Q1: Write pyspark code to create dataframe and print with ‘color’ and ‘weight’ as separate columns. inventoryData = [ (‘Laptop’, 20, {‘color’: ‘silver’, ‘weight’: 2.5}), (‘Phone’, 50, {‘color’: ‘black’, ‘weight’: 0.5}), (‘Tablet’, 30, {‘color’: ‘white’, ‘weight’: 0.8}), (‘Chair’, 10, {‘color’: ‘brown’, ‘weight’: 3.0}), (‘Printer’, 15, {‘color’: ‘gray’, ‘weight’: 7.5}), ] schema =[“Product”, “Quantity”, “Properties”] Product … Read more