Note: Many of these questions were specifically asked based on my work experience and the tools I have worked with. It may differ as per your experience. Explain the entire architecture of Trino. What happens when you submit a query? Explain the architecture of iceberg. What is compaction in iceberg? How do you enable the kafka events in Trino? Differences between Flink and Spark Streaming. When would you use which? Name the operators used in Flink in stream joins. If you have a spark job to process a 10GB csv file, and you have 2 spark executors with 2GB each and 2 cores, will it be able to process it successfully? If yes, are there observed issues/anomalies? If no, why? For a kafka topic where the consumer has a high lag, what will you do to reduce that lag? Will just increasing partitions by sufficient of the consumers are same? How can you delete specific events from a kafka topic? If you have a hive table, which will run faster queries on it, Hive or Trino? Do you need Trino in this usecase when you just have hive tables? If the query is a simple SELECT COUNT(*) FROM table, which will execute it faster? Let's say, you observed Trino was faster. Why would that be? What are the compute engines you can use? What is Tez? What does it mean by the term "split" printed in the Trino logs.