Real questions from top companies in Spark/Big Data
Describe the role of a DAG Scheduler in PySpark
Describe the role of a workflow orchestrator like Airflow in a data pipeline.
Describe the stages of a Spark job and strategies to optimize Spark performance for large datasets.
Describe your approach to managing offsets in Kafka.
Design an ETL pipeline using Kafka and Spark Streaming
Difference between Presto vs. Spark underlying architecture
Discuss Delta Logs file format and its significance.
Discuss common transformations used in Spark code.
Discuss file formats (Parquet, Avro, ORC) and storage strategies.
Discuss how you integrated Azure services into your Spark application.
Discuss performance tuning concepts such as shuffle, skew, and caching.
Discuss stages and tasks in a Spark execution plan.
Discuss techniques such as partitioning, broadcast joins, and caching to enhance Spark job performance.
Discuss the process of moving files in Databricks File System (DBFS).
Executor vs Driver in Spark
Explain Apache Spark fundamentals, OOM scenarios and their resolutions, optimization techniques, strategies for optimized joins, and handling data skewness with Key Salting techniques.
Explain Azure Databricks architecture and its integration with other Azure services.
Explain Bronze/Silver/Gold Layers.
Explain Delta Live Tables and their features, such as declarative pipeline definition and automatic data validation.
Explain Delta Table features – Z-ordering and Time Travel.
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