Real questions from top companies · hard
How would you fetch data from an API and load it into a DataFrame?
How would you handle a large-scale data shuffle in a Dataflow pipeline?
How would you handle memory management in Spark?
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?
How would you move a file to another path in Databricks File System (DBFS)?
How would you optimize Glue jobs to reduce processing time for large datasets?
How would you optimize Spark jobs for better performance?
How would you optimize a Spark job that takes too long to run in production?
How would you optimize a slow-running notebook in Databricks?
How would you optimize your Spark Streaming ETL pipeline for high throughput and low latency?
How would you read a large file (e.g., 15GB) efficiently in Spark by increasing parallelism?
How would you read data from an RDBMS using Spark? Provide the syntax.
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.
Implement a Kafka consumer that writes streaming data into a database.
Implement a PySpark job to read CSV data, perform joins, and store output as partitioned Parquet.
Importance of each layer in Databricks.
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