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Top Top 50+ Data Engineer Interview Questions Interview Questions | CandidateToHR

Crack your next Data Engineering interview with our expert guide. 50+ questions and answers covering SQL, Python, Spark, ETL, Cloud, and system design.

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Data Engineering is a highly technical discipline requiring expertise in software engineering, database design, and distributed systems. Prepare for your technical interviews with these 50 comprehensive questions and answers.

Frequently Asked Questions

What is the difference between clustered and heap tables?

Clustered tables physically sort and store data based on their index key. Heap tables store rows in arbitrary order, requiring index pointers for lookups.

What is data skew and how does it affect Spark?

Data skew is the uneven distribution of data across partitions. It causes a few executor nodes to work significantly longer than others, creating execution bottlenecks.

How does map-side join work?

A map-side join broadcasts a small table to all executors, allowing them to join datasets locally without executing a network shuffle of the larger table.

What is the difference between batch and streaming pipelines?

Batch pipelines process static blocks of data gathered over time. Streaming pipelines process continuous feeds of live events in real-time.

How do you handle schema changes in upstream databases?

I use schema validation layers, direct records failing schemas to a dead-letter queue, and coordinate migrations using two-phase updates.

Why is Parquet preferred over CSV in big data systems?

Parquet is a columnar storage format, enabling high compression, column pruning, and projection pushdown, making query execution significantly faster.

What is the role of an orchestrator like Airflow?

An orchestrator schedules tasks, manages dependency flows, handles retries, tracks execution logs, and sends alerts for failed workflows.

What is the difference between SQL and NoSQL?

SQL databases are relational and structured with strict schemas. NoSQL databases are schema-less, scale horizontally, and use document or key-value structures.

What are index keys and why do they speed up database queries?

Index keys are database tree structures providing direct pointers to row locations, avoiding slow full-table scans during search operations.

How can you identify bottleneck steps in a Spark job?

I analyze performance graphs and execution timelines using the Spark UI, looking for skewed tasks, long garbage collection times, and excessive shuffle writes.