Real questions from top companies in Spark/Big Data · hard
How to fill null values in PySpark?
How to handle null value in a single column in PySpark?
How to optimize mappers using properties in MapReduce?
How to remove duplicates in PySpark?
How would you debug a failing Spark job running on Dataproc?
How would you debug a slow-running PySpark job? What factors would you investigate?
How would you design a Kafka-based pipeline for processing streaming data in real-time?
How would you design a scalable and fault-tolerant data processing pipeline for handling large volumes of streaming data?
How would you enforce encryption at rest for all objects in a bucket?
How would you ensure exactly-once processing for Kafka consumers in your Spark job?
How would you ensure the pipeline is scalable for larger datasets?
How would you fetch data from an API and load it into a DataFrame?
How would you handle a large-scale data shuffle in a Dataflow pipeline?
How would you handle memory management in Spark?
How would you handle unstructured data in Hive?
How would you identify and resolve a shuffle spill in Spark UI?
How would you manage the streaming data schema and handle schema evolution in Delta Lake?
How would you manage transitions to Glacier Instant Retrieval and Deep Archive?
How would you migrate metadata from Hive Metastore to Glue?
How would you move a file to another path in Databricks File System (DBFS)?
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