Real questions from top companies in Spark/Big Data
Explain the Medallion architecture and its benefits in data engineering.
Explain the architecture and role of the Hive Metastore in a data pipeline
Explain the architecture of Databricks, including the control plane and data plane.
Explain the architecture of Kafka
Explain the architecture of Kafka and its core components.
Explain the architecture of Spark Streaming
Explain the architecture of Spark, including its components such as driver, executor, and cluster manager.
Explain the architecture of Spark, including the roles of driver, executors, DAGs, and SparkContext.
Explain the benefits of using columnar storage formats like Parquet or ORC.
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 differences between Spark's shuffle and broadcast join. When would you use each?
Explain your approach to monitoring and logging Spark jobs in AWS. What tools would you use to identify performance bottlenecks?
How did you handle data ingestion and processing for large datasets?
How do you compare the time investment and value of a task?
How do you handle bad data in Databricks?
How do you handle out-of-memory errors in Spark jobs?
How do you handle very large datasets in Spark to ensure scalability and efficiency?
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