Real questions from top companies in Spark/Big Data · hard
Walkthrough Spark's architecture, focusing on driver, executors, and DAGs
What are Spark optimizations, and can you explain them?
What are the challenges of implementing real-time analytics using Spark Streaming?
What are the key properties of Delta Lake that differentiate it from traditional data lakes?
What happens if the checkpoint location is accidentally deleted?
What insights can you gather from the DAG visualization in Spark UI?
What is Predicate Pushdown and AQE with Example
What is a serializer in Spark?
What is data shuffling in Spark, and how do you minimize its impact on job performance?
What is the difference between Lazy Evaluation and Eager Execution in PySpark?
What is the difference between MapReduce and Spark?
What is the difference between Pandas DataFrame and Spark DataFrame? When would you prefer using each?
What is the importance of the checkpoint location in Databricks?
What is the salting technique, and when would you use it?
What performance optimization techniques have you applied in Spark, Sqoop, or Databricks?
What role does Kafka play in real-time data streaming pipelines?
What role would Kafka or similar event-driven platforms play in your architecture?
What strategies would you use to reduce latency in a streaming data pipeline?
What trade-offs would you consider when choosing between batch processing and real-time streaming?
When submitting Spark jobs, how does the process work in the backend? Explain.
Type or paste your answer to any of these questions and our AI Coach scores it, highlights gaps, and rewrites it at FAANG quality. Free to try.