What is Apache Spark and how does it differ from Hadoop MapReduce?

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Data Engineering Interview Questions

  1. [JUNIOR] What is the ETL process?
  2. [JUNIOR] What is data engineering and what are the main responsibilities of a data engineer?
  3. [JUNIOR] What is a data pipeline?
  4. [JUNIOR] What is a data warehouse?
  5. [JUNIOR] What is the difference between a data lake and a data warehouse?
  6. [MID] What are the key differences between ETL and ELT, and when would you choose one over the other?
  7. [JUNIOR] What are the main differences between SQL and NoSQL databases?
  8. [JUNIOR] What is the difference between batch processing and stream processing?
  9. [JUNIOR] What is database indexing and when would you use it?
  10. [MID] What is data partitioning and how does it improve query performance and cost efficiency?
  11. [SENIOR] How do you design ETL pipelines to ensure idempotency?
  12. [SENIOR] How do you approach optimizing query performance in a data warehouse?
  13. [JUNIOR] What is normalization in database design?
  14. [JUNIOR] What is data modeling and what are the main types of data models?
  15. [JUNIOR] What is Apache Spark and how does it differ from Hadoop MapReduce?
  16. [JUNIOR] What is Apache Kafka and why is it used in data pipelines?
  17. [JUNIOR] What is a star schema?
  18. [JUNIOR] What is the difference between structured and unstructured data?
  19. [MID] What is the slowly changing dimension (SCD) and what are the different types?
  20. [MID] What is data lineage and why is it important in data engineering?
  21. [MID] How does columnar storage improve query performance compared to row-based storage?
  22. [MID] What is schema evolution and why does it matter in data pipelines?
  23. [MID] How do you handle duplicate records in ETL workflows?
  24. [MID] What is Apache Airflow and why is it popular for pipeline orchestration?
  25. [MID] What are the advantages and disadvantages of denormalization?
  26. [SENIOR] How would you design a data pipeline that handles both batch and stream processing workloads?
  27. [SENIOR] How do you handle data skew in distributed processing systems?
  28. [SENIOR] How do you ensure data consistency in distributed systems?
  29. [SENIOR] How do you design pipelines to ensure reliability and observability at scale?
  30. [SENIOR] What strategies do you use to handle late-arriving or out-of-order data in pipelines?
  31. [SENIOR] How do you decide between a data lake, data warehouse, and lakehouse architecture?
  32. [SENIOR] How would you optimize a slow-running SQL query on a large dataset?
  33. [JUNIOR] What is a snowflake schema and how does it differ from a star schema?
  34. [JUNIOR] What is the difference between a data engineer and a data scientist?
  35. [JUNIOR] What are Common Table Expressions (CTEs) in SQL and why are they useful?
  36. [MID] What is Change Data Capture (CDC) and how is it used in data pipelines?
  37. [MID] What are the trade-offs between Avro, Parquet, and JSON serialization formats?
  38. [MID] What is the Lambda architecture and what problem does it solve?
  39. [MID] How does JOIN order affect SQL query performance?
  40. [SENIOR] How would you design a schema evolution strategy for a data lake?
  41. [SENIOR] How would you implement an incremental update mechanism in a daily ETL pipeline?
  42. [SENIOR] How do you approach data pipeline testing and validation?
  43. [SENIOR] How does dbt fit into the modern data stack?
  44. [SENIOR] How do you implement data partitioning in cloud warehouses like BigQuery, Redshift, or Synapse?
  45. [EXPERT] What do exactly-once semantics mean in streaming systems and how are they achieved?
  46. [JUNIOR] What is a data mart and how does it relate to a data warehouse?
  47. [JUNIOR] What are the Four Vs of Big Data?
  48. [JUNIOR] What is the difference between DELETE and TRUNCATE in SQL?
  49. [JUNIOR] What is the difference between UNION and UNION ALL in SQL?
  50. [MID] What are surrogate keys and why are they used in data warehouses?
  51. [MID] What is data governance and what does it encompass?
  52. [MID] What is the difference between stream processing and micro-batching?
  53. [MID] What is Apache Flink and how does it compare to Spark Streaming?
  54. [MID] What is PySpark and when would you use it over plain Python?
  55. [MID] What is the difference between WHERE and HAVING in SQL?
  56. [SENIOR] How would you handle a large-scale backfill of data without disrupting production workloads?
  57. [SENIOR] What strategies can reduce cloud costs in ETL and ELT pipelines?
  58. [SENIOR] What is your approach to monitoring and alerting in data systems?
  59. [SENIOR] How do you approach capacity planning for data infrastructure?
  60. [SENIOR] What are the pros and cons of orchestration tools like Airflow versus managed services like AWS Step Functions?
  61. [SENIOR] What are common challenges in designing schemas for clickstream or event data?
  62. [EXPERT] How would you design a globally distributed and highly available data pipeline?
  63. [EXPERT] How would you design a system to guarantee data integrity in a microservices architecture?
  64. [EXPERT] What is watermarking in stream processing and why is it important?
  65. [EXPERT] How do you ensure fault tolerance in real-time data systems?
  66. [EXPERT] How would you handle schema evolution in an ETL pipeline that extracts data from constantly changing APIs?
  67. [JUNIOR] What is a stored procedure and when would you use one?
  68. [JUNIOR] What is MapReduce and how does it work?
  69. [MID] What is data masking and when would you use it?
  70. [MID] What is role-based access control (RBAC) in the context of data systems?
  71. [SENIOR] How do you reconcile differences between source and warehouse data?
  72. [SENIOR] How would you use tools like dbt or Great Expectations to enforce data quality in a pipeline?
  73. [EXPERT] What are stateful operations in stream processing and how are they managed?
  74. [EXPERT] How would you implement large-scale distributed join operations without running out of memory?
  75. [EXPERT] What are the challenges and solutions for real-time anomaly detection in high-velocity data streams?
  76. [EXPERT] How do you handle data skew using techniques like salting before join operations?
  77. [EXPERT] What is the difference between SLAs and SLOs in data engineering contexts?
  78. [EXPERT] What are the implications of cross-cloud data migration and what strategies minimize downtime?
  79. [EXPERT] How would you approach designing a Data as a Service (DaaS) platform?
  80. [EXPERT] How would you implement a scalable and efficient data versioning system?