Real questions from top companies in Spark/Big Data
How do you handle out-of-memory errors in Spark jobs?
How do you handle schema evolution in Spark, especially when reading data from sources like Parquet or Avro?
How do you handle very large datasets in Spark to ensure scalability and efficiency?
How do you help stakeholders query Delta Lake tables? What tools and approaches?
How do you identify skewed partitions in a dataset?
How do you implement incremental updates in a data lake using AWS services and Spark?
How do you implement row and column-level security in Databricks?
How do you initiate a DAG in Airflow?
How do you manage dependencies between tasks in a Cloud Composer DAG?
How do you manage memory allocation in Spark?
How do you manage schema changes in PySpark when processing data over time?
How do you monitor Spark jobs?
How do you monitor and debug Spark applications in production?
How do you move a Databricks notebook to higher environments?
How do you optimize a join operation in Spark for large datasets?
How do you optimize long-running PySpark scripts on EMR?
How do you prioritize your tasks in a multi-project environment?
How do you reduce shuffle operations in Spark?
How do you resolve merge conflicts in Databricks notebooks?
How do you set up CI/CD for a PySpark ETL workflow?
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