Hard-level spark & big data questions from real data engineering interviews.
These hard spark & big data questions are selected from real interviews at top companies. Each question includes a detailed expert answer and pro tip to help you nail your interview.
What is the difference between SparkSession and SparkContext in Spark?
Can you explain the architecture of Apache Spark and its components?
Describe the difference between Spark RDDs, DataFrames, and Datasets.
How does Spark's Catalyst Optimizer work? Explain its stages.
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.
Architecturally, how would you justify or challenge Hadoop vs. a cloud-native data lake (S3 + EMR/Databricks) for a greenfield enterprise data platform? Discuss scalability ceilings, cost model trade-offs, and operational complexity.
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.
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.
Explain the Medallion Architecture (Bronze, Silver, Gold layers).
Explain the benefits of using DataFrames over RDDs.
Explain the concept of checkpointing in Spark and why it is important.
Explain the difference between batch and streaming data processing in Data Fusion.
Given a streaming dataset from Kafka, how would you ingest the data in real-time using Spark?
How do you optimize Spark jobs for performance?
How would you implement a sliding window aggregation in Spark Structured Streaming?
Implement a Spark job to find the top 10 most frequent words in a large text file.
What are the key components of the Spark execution model (Job, Stage, Task)?
What is Spark's Catalyst Optimizer? Explain its stages.
What is the difference between Spark RDDs, DataFrames, and Datasets?
What is the small-file problem in Spark, and how do you solve it?
Why is SparkSession used in Spark 2.0 and later versions?
A data pipeline processes files for different clients stored in separate directories. Explain how you would use dynamic DAG creation to handle client-specific workflows in Airflow.
Adaptive Query Execution (AQE): Discuss how AQE optimizes query execution in Spark dynamically based on runtime stats.
After cleaning, how would you store the transformed data into Delta Lake?
Alternatives to the Medallion Architecture
Apache Spark Architecture - RDD, DAG, cluster manager, driver node, worker node
Apache Spark Fundamentals - discuss
Apache Spark Fundamentals - failures, job optimization, resource utilization
Basic Spark commands – Create RDD, Load data, Filter
Bloom Filters in Spark projects - explain use case
Cache vs. Persistent storage in Spark?
Calculating Databricks costs - explain DBU
Can Presto work with Near Real-Time Data (Streaming Data Source)?
Can you explain how streams and tasks handle data freshness in near real-time?
Challenges with Spark Jobs and Resolutions
Compare Hadoop and Spark. Which one would you choose for a real-time application, and why?
Compare Kafka Streams and Spark Structured Streaming for real-time processing
Compare Kafka and RabbitMQ for real-time message processing in a streaming platform.
Conceptualize and design a real-time streaming data pipeline end-to-end.
Databricks - platform, use cases
Define what a User-Defined Function (UDF) is and how to register it in PySpark.
Delta Lake: ACID compliance, time travel, streaming support
Describe how you would monitor ETL job performance and handle long-running tasks.
Describe how you would optimize a join between two large tables where one is significantly smaller, using broadcast joins in PySpark.
Describe how you would optimize slow-running Spark jobs in a distributed environment.
Describe the projects emphasizing Spark, Hadoop, or Azure for large-scale data processing
Describe the role of a DAG Scheduler in PySpark
Describe the stages of a Spark job and strategies to optimize Spark performance for large datasets.
Design an ETL pipeline using Kafka and Spark Streaming
Difference between Presto vs. Spark underlying architecture
Discuss common transformations used in Spark code.
Discuss file formats (Parquet, Avro, ORC) and storage strategies.
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