Real questions from top companies in Spark/Big Data · hard
How many stages are created in a Spark job, and how are they formed?
How to Connect to Salesforce Without Typing Credentials Manually
How to Upsert Your Data Daily Using Spark
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 handle a large-scale data shuffle in a Dataflow pipeline?
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?
If a consumer fails to process a message due to data corruption, describe how you would configure Kafka to handle retries and avoid message loss.
In Spark, what is the difference between cores and executors?
Share your experience in working with big data technologies such as Hadoop, Spark, or AWS EMR. How have you leveraged these tools in your previous projects?
Spark Architecture - Components include Driver, Executors, Cluster Manager, and Tasks
Spark Tungsten & Catalyst Optimizer
Steps to link a Databricks notebook to an ADF pipeline
Trade-offs between batch processing (Spark) vs. real-time streams (Kafka)
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