Real questions from top companies in Spark/Big Data
Explain the benefits of using columnar storage formats like Parquet or ORC.
Explain the concept of RDD, DataFrame, and Dataset in PySpark.
Explain the concept of consumer groups in Kafka. How do they affect message processing?
Explain the concept of preemptible VMs in Dataproc and their cost implications.
Explain the configuration of a Spark cluster for optimal performance
Explain the difference between TriggerDagRunOperator and ExternalTaskSensor in Airflow.
Explain the difference between coalescing and repartitioning in Spark
Explain the differences between Spark's shuffle and broadcast join. When would you use each?
Explain the impact of Vacuum and Analyze operations on performance.
Explain the role of DAGs (Directed Acyclic Graphs) in Spark.
Explain your approach to monitoring and logging Spark jobs in AWS. What tools would you use to identify performance bottlenecks?
Explain your choice of streaming framework (Kafka, Spark Streaming, etc.).
Fault Tolerance in Spark vs. Hadoop?
Given a DataFrame with columns id and name, add a new column department: If id < 100 assign HR, if id >= 100 and id < 200 assign admin.
Given two DataFrames, perform specified data transformations and store the result in a new DataFrame
GroupByKey vs ReduceByKey – Differences and performance implications?
Handling Skewness in Data - salting, broadcast join
Handling custom data types in Spark
Have you worked with UDFs in Spark? When do you use them, and how do they differ from built-in functions?
Have you worked with data compaction in Delta Lake?
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