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Data engineering interviews often focus on practical, real-world scenarios to assess hands-on experience. Below are common questions and detailed technical insights to help you prepare effectively.
Common Data Engineering Interview Questions
- What cluster manager have you used in your project?
– Example: Apache YARN, Kubernetes, or Mesos.
– Command to check YARN cluster status:
yarn node -list
2. What is your cluster size?
- Example: 10-worker nodes, 64GB RAM each, 16 cores.
- Check cluster resources in Spark:
spark.sparkContext.getConf().getAll()
- How does your data arrive at your storage location?
– Example: Kafka streams, SFTP, or API ingestion.
– Kafka consumer command:
kafka-console-consumer --bootstrap-server localhost:9092 --topic test_topic
4. What optimization techniques have you used?
- Example: Partitioning, caching, broadcast joins.
- Optimize a Spark DataFrame:
df.repartition(10).cache()
5. Explain the `spark-submit` command.
- Sample command:
spark-submit --master yarn --deploy-mode cluster --executor-memory 8G app.py
- How do you handle job failures in production?
– Example: Airflow retries, monitoring with Prometheus.
– Airflow retry config:
default_args = {'retries': 3, 'retry_delay': timedelta(minutes=5)}
7. How do you reprocess failed data?
- Example: Idempotent workflows, checkpointing.
- Spark checkpointing:
spark.sparkContext.setCheckpointDir("hdfs://path/to/checkpoint")
You Should Know: Debugging & Performance Tuning
- Debugging slow Spark jobs? Use:
spark.ui.port=4040
Access Spark UI at
http://<driver-node>:4040. Skewed data handling:
-- Salting technique in SQL SELECT , CONCAT(key, '_', FLOOR(RAND() 10)) AS salted_key FROM table;
Monitor resource usage:
top -H -p <spark_pid>
What Undercode Say
Mastering data engineering requires hands-on expertise in distributed systems, optimization, and automation. Key takeaways:
– Use `spark-submit` efficiently.
– Optimize with partitioning, caching, and broadcast joins.
– Automate recovery using Airflow or CI/CD pipelines.
– Monitor with Spark UI, YARN, and Linux tools (htop, iostat).
Expected Output:
A well-prepared data engineer should be able to:
- Explain cluster management (YARN/K8s).
- Optimize Spark jobs (memory, parallelism).
- Handle failures (idempotency, checkpoints).
- Debug using logs and monitoring tools.
For further reading, check:
(Word count: ~70 lines)
References:
Reported By: Abhisek Sahu – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
