Data Engineer Interview Questions

Sreeram reddy B. - "select employeename, employeeid, salary, department, DR from ( select employeename, employeeid, salary, dense_rank() over (partition by department order by salary desc) DR, department from employee ) where DR <=3 order by department, DR"See full answer

Sreeram reddy B. - "user table - with userid, username, email, phonenumber, accountcreateddate exercises table - types of exercises - indoor walk, outdoor walk, running, stairs, cycling, swimming etc - exerciseid, exercisetype date table - date, day, month, year - with dateid Session table - userid, sessiondateid(linked to dateid in date table), exerciseid, distance covered, calories spent, starttime, endtime "See full answer

Hayatu H. - "How do you find consecutive days for login (MySQL, SQL, date, subquery, MySQL 5.7, development)? 1 Follow Request Answer More All related (34) Recommended 📷 Trausti Thor Johannsson · Follow Been using MySQL for more than 16 yearsDec 27 There are functions like DATEDIFF but there are also BETWE"See full answer

Bala G. - "SELECT s.Sale_Date, SUM(si.Quantity * si.SalePrice) AS TotalRevenue FROM Sales s JOIN SaleItems si ON s.SaleID = si.Sale_ID GROUP BY s.Sale_Date ORDER BY s.Sale_Date; "See full answer

Hayatu H. - "What do all data scientists need to know about how to work with very large datasets? 37 Follow Request Answer More All related (39) Recommended 📷 Corrin Lakeland · Follow , M.S. Data Science, University of St. Thomas, St. Paul (2018)6yData Science consultant and managerUpvoted by[Tom Halloin](https://www.quora"See full answer

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