Top Data Engineer Interview Questions

Reno S. - "func isMatch(text: String, pattern: String) -> Bool { // Convert strings to arrays for easier indexing let s = Array(text.characters) let p = Array(pattern.characters) guard !s.isEmpty && !p.isEmpty else { return true } // Create DP table: dpi represents if s[0...i-1] matches p[0...j-1] var dp = Array(repeating: Array(repeating: false, count: p.count + 1), count: s.count + 1) // Empty pattern matches empty string dp[0]["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

Chen J. - "Leetcode 347: Heap + Hashtable Follow up question: create heap with the length of K instead of N (more time complexity but less space )"See full answer

Mohith J. - "Recursion: 0 if NULL, else 1+max(height(left), height(right))"See full answer

Meenakshi D. - "BETWEEN and HAVING clauses in SQL serve different purposes: 1. BETWEEN Clause Used to filter rows based on a range of values. Works with numeric, date, or text values. Can be used with WHERE or HAVING clauses. The range includes both lower and upper bounds. Example: Filtering employees with salaries between 30,000 and 50,000 `SELECT * FROM Employees WHERE salary BETWEEN 30000 AND 50000;` 2. HAVING Clause Used to filter **groups"See full answer

Anonymous Wasp - "I was able to provide the optimal approach and coded it up"See full answer

Rajeev K. - "I’ve spent over 6 years building and scaling e-commerce products across EMEA and APAC. At Jumia, I led product initiatives on the checkout and payments side. For example, I launched gamified promotions on PDP and checkout that improved engagement and delivered a 2.3x uplift in conversion. I also introduced automated installment payments and order cancellation flows, which not only improved user trust but also reduced complaints by 30% and lowered operational costs. Before that, at Lazada, I work"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

Erjan G. - "a process can include many threads. good for concurrent and parallel task execution"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

Md kamrul H. - "public class HashMap { public class Element { T key; V value; Element(T k, V v) { this.key = k; this.value = v; } } private static final int DEFAULT_CAPACITY = 16; private static final float LOAD_FACTOR = 0.75f; private LinkedList[] table = new LinkedList[DEFAULT_CAPACITY]; private int size = 0; private int threshold = (int) (DEFAULTCAPACITY * LOADFACTOR); public void put(T k"See full answer

Ramagiri P. - "Medallion architecture is a layered data architecture used in lakehouse systems. Data flows through Bronze, Silver, and Gold layers where each layer improves data quality. Bronze stores raw data, Silver contains cleaned and validated datasets, and Gold provides aggregated business-ready data for analytics and reporting bronzedf = spark.read.json("/landing/apidata") bronze_df.write.format("delta").save("/bronze/users")"See full answer

Erjan G. - "i said there is hashed, clustered, non-clustered"See full answer

Dessalew A. - "Parquet = reading only the columns you need in a spreadsheet Avro = reading full rows one at a time"See full answer

Anzhe M. - "Not my answer, but rather the details of this question. It should include the following functions: int insertNewCustomer(double revenue) -> returns a customer ID (assume auto-incremented & 0-based) int insertNewCustomer(double revenue, int referrerID) -> returns a customer ID (assume auto-incremented & 0-based) Set getLowestKCustomersByMinTotalRevenue(int k, double minTotalRevenue) -> returns customer IDs Note: The total revenue consists of the revenue that this customer bring"See full answer