Spark & Big Data questions from Swiggy data engineering interviews.
These spark & big data questions are sourced from Swiggy data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (8 of 11 are tagged hard). Recurring themes are spark, partition, and window — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Incedo and Fragma Data Systems, so the preparation transfers across companies. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 11 curated questions: 2 easy, 1 medium, and 8 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are spark (8), partition (7), window (4), optimization (4), join (1), and sql (1). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
How do you handle late-arriving data in Spark Structured Streaming?
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.
How would you implement a sliding window aggregation in Spark Structured Streaming?
Compare HDFS and cloud-based storage systems in terms of scalability and performance.
Describe how you would use PySpark to aggregate and summarize large transaction datasets.
Describe the role of a workflow orchestrator like Airflow in a data pipeline.
Describe the stages of a Spark job and strategies to optimize Spark performance for large datasets.
Explain how Kafka handles real-time data streaming and guarantees message delivery.
Provide strategies for handling data deduplication and cleaning in Spark jobs.
Walk through how you would debug the data ingestion process to identify slow stages.
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