The most frequently asked partition questions in data engineering interviews.
Master partition for your next data engineering interview. These questions cover core concepts, advanced patterns, and real-world scenarios that interviewers test.
Tell me about yourself and your experience.
What is the difference between repartition and coalesce in Apache Spark?
Write an SQL query to find the second-highest salary from an employee table.
What is the difference between cache() and persist() in Spark? When would you use each?
What is the difference between groupByKey and reduceByKey in Spark?
What is the difference between narrow and wide transformations in Apache Spark? Explain with examples.
What architecture are you following in your current project, and why?
Demonstrate the difference between DENSE_RANK() and RANK()
Explain the differences between Data Warehouse, Data Lake, and Delta Lake
Explain the differences between Repartition and Coalesce. When would you use each?
What is the difference between partitioning and bucketing in Spark, and when would you use bucketing?
What strategies can you use to handle skewed data in Spark?
Briefly introduce yourself and walk us through your journey as a Data Engineer so far.
Describe a scenario where partitioning and bucketing would improve query performance.
Explain the types of triggers in ADF, including schedule, tumbling window, and event-based triggers.
How do you remove duplicate rows in BigQuery?
Joins and window functions - INNER, LEFT, RIGHT, FULL OUTER, ROW_NUMBER(), RANK(), DENSE_RANK()
Can you explain the architecture of Apache Spark and its components?
Describe the difference between Spark RDDs, DataFrames, and Datasets.
Explain the difference between Spark's map() and flatMap() transformations.
What is the small-file problem in Spark, and how do you solve it?
How do you optimize Spark jobs for better performance? Mention at least 5 techniques.
Tell me about a time when you faced a challenging situation at work and how you handled it.
What challenges did you face, and how did you tackle them?
What would you do if a pipeline failed and you couldn't find the reason?
What is Snowflake's architecture, and why is it unique?
Briefly explain the architecture of Kafka.
Describe the data pipeline architecture you've worked with.
Have you worked on Data Warehousing projects?
How would you read data from a web API? What steps would you follow after reading the data?
Retrieve the most recent sale_timestamp for each product (Latest Transaction).
What is the difference between OLTP and OLAP?
Difference Between Internal and External Tables in BigQuery
Difference between ROW_NUMBER(), RANK(), and DENSE_RANK() with examples.
Explain SQL Window Functions with examples.
Explain the use of the MERGE statement in SQL.
How do you optimize a long-running SQL query?
How would you handle duplicate records in an SQL table?
Implement a query to find the top 5 customers by total sales amount.
SQL query to find the second highest salary from each department.
Write an SQL query to find duplicate emails in a users table.
Triggers in ADF, especially tumbling window triggers.
What is a window function? Explain with an example.
Write a SQL query to find top 3 earners in each department.
Write a query to find the top three highest-paid employees in each department using window functions.
Write complex SQL queries involving multiple joins, subqueries, and data aggregation logic.
Convert complex SQL (CTEs, window functions, subqueries) to production-grade PySpark. Discuss when to use spark.sql() vs. DataFrame API, and the implications for testability, partitioning, and execution predictability.
Design a cost-aware resource strategy for a Databricks workload with spiky and batch jobs. Explain Dynamic Resource Allocation, when to disable it, and how min/max executors and spot instances affect cost and SLAs.
Design an anti-skew strategy for a join on a high-cardinality key with a long-tail distribution (e.g., a few keys hold 80% of rows). Cover salting, split-skew, AQE, and cost/operational trade-offs.
Prioritize Spark optimizations by impact and effort. Discuss partitioning strategy, caching policy, join selection, shuffle reduction, and when each becomes a scalability or cost bottleneck.
Explain how Adaptive Query Execution changes the economics of Spark tuning. What problems does it solve at runtime, and when might you still need manual intervention (e.g., salting, broadcast hints)?
Walk through the three AQE features in Spark 3.x (coalesce, join switch, skew join)—how they operate at shuffle boundaries, which configs enable them, and what happens when AQE cannot help.
Explain wide vs. narrow transformations and how they drive shuffle cost, failure domains, and pipeline design. When would you intentionally add a wide transformation, and how do you minimize its impact?
Design a Delta table layout for mixed workload: point lookups by user_id, range scans by date, and full partition scans. Compare partitioning vs. Z-ordering—when to use each, and the rewrite cost trade-off.
Architecturally, how do Job–Stage–Task boundaries in Spark's execution model impact cluster sizing, shuffle cost, and when would you deliberately collapse or split stages?
Design a fault-tolerant Spark Streaming checkpoint strategy: what to persist, recovery semantics, and cost/scalability trade-offs with checkpoint frequency.
Architect incremental load in ADF + Databricks with idempotency, late-arrival handling, and cost/scalability implications of watermark vs. change data capture.
Explain strategies for managing schema changes in PySpark over time.
Explain the Medallion Architecture (Bronze, Silver, Gold layers).
Explain the benefits of using DataFrames over RDDs.
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