Real questions from top companies in Spark/Big Data · hard
Explain the differences between Spark's shuffle and broadcast join. When would you use each?
How did you handle data ingestion and processing for large datasets?
How do you resolve merge conflicts in Databricks notebooks?
How do you set up CI/CD for a PySpark ETL workflow?
How do you store streaming data in Delta Lake and handle schema evolution?
How do you use Spark UI to debug stages, tasks, and performance issues?
How does Adaptive Query Execution (AQE) work?
How does Auto Loader avoid reloading files with the same name?
How does Autoscaling work in Databricks and what are its benefits?
How does Data Flow optimize data transformations for large datasets?
How does Databricks create clusters for running Spark jobs?
How does Databricks integrate with external storage systems?
How does Delta Lake store the transaction history in S3 buckets?
How does Glue Catalog handle schema versioning compared to Hive Metastore?
How does Kafka ensure message durability and reliability?
How does Optimize command improve query latency in Delta tables?
How does Spark execute a job? Explain the DAG and stages.
How does lazy evaluation work in Spark?
How does the driver program handle task scheduling?
How is Git version control implemented in Databricks?
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