Data engineering interview questions · medium
Write a Python function to check if a string is a palindrome.
Write a Python function to check if a string is a palindrome.
Create a Python program to demonstrate the use of set operations (union, intersection).
Describe Spark's memory management model. How do you handle heap memory overhead issues?
Differentiate SORT BY, ORDER BY, DISTRIBUTE BY, and CLUSTER BY
Extended the solution to determine the nth largest element in an array.
GeoPandas - definition and features
Grouping and aggregation functions?
How many cities does each department operate in? List the top 3 departments in terms of the most number of cities. In case of a tie, order by dept_id.
How would you decide between using DISTKEY and SORTKEY?
Implement an algorithm to find the longest common prefix among an array of strings.
List customers with more than 5 orders.
List every combination of dept_name, employee_name, and city such that the employee belongs to the department and the same city in which the department is located.
Multithreading and Synchronization in Java - write code to manage synchronized threads
Replace words and perform string operations in Python (replace, vowel removal, word count, pattern check).
Reverse a string with special characters preserved.
Sort and merge arrays
Spark Coding: Using explode() Function to flatten nested arrays
Stuff Function for XML Usages
What role does the executor heap size play in preventing OOM errors?
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Data engineering Python rounds focus on: PySpark DataFrame operations, pandas data manipulation, file I/O and JSON/CSV parsing, API integrations, basic algorithms and data structures, error handling patterns, and writing Airflow DAGs or pipeline code.
Generally yes. Data engineering Python rounds rarely include LeetCode-hard algorithm problems. Instead, they test practical data manipulation, PySpark operations, and pipeline-oriented code. However, some FAANG companies still include a standard coding round.
Learn both. PySpark is tested for distributed processing scenarios (large datasets, Spark cluster operations). Pandas is tested for smaller-scale data manipulation and analysis. Most interviewers expect fluency in both, with PySpark being more critical for senior roles.