Data Engineer Data Pipeline Design Interview Questions

Kshitij I. - "Data lake and warehouse are both places that allow an organization to store large amounts of data. When swimming in a lake, one would imagine that they come across all sorts of stuff - floating twigs, fish in the water, stones, chemicals and sometimes may be even a snake. Similarly, a data lake stores all forms of data that the company has without any indexing. The data is available at any time but needs to be first cleaned up and reorganized before it can be used for any type of analysis. A"See full answer

Joshua R. - "Hadoop is better than PySpark when you are dealing with extremely large scale, batch oriented, non-iterative workloads where in-memory computing isn't feasible/ necessary, like log storage or ETL workflows that don't require high response times. It's also better in situations where the Hadoop ecosystem is already deeply embedded and where there is a need for resource conscious, fault tolerant computation without the overhead of Spark's memory constraints. In these such scenarios, Hadoop's disk-b"See full answer

Anzhe M. - "There are 2 questions popping into my mind: Should the 2nd job have to kick off at 12:30AM? Are there others depending on the 2nd job? If both answers are no, we may simply postpone the second job to allow sufficient time for the first one to complete. If they are yeses, we could let the 2nd job retry to a certain amount of times. Make sure that even reaching the maximum of retries won't delay or fail the following jobs."See full answer