Real questions from top companies in System Design/Architecture
Discuss your experience with ETL (Extract, Transform, Load) processes. What tools and techniques have you used to ensure efficient data extraction and transformation?
Explain AWS Glue Data Catalog.
Explain Spark's fault tolerance mechanisms.
Explain batch vs real-time processing choices and their trade-offs.
Explain deployment architecture for big data.
Explain how Spark handles fault tolerance. How does it recover from node failures?
Explain how serverless computing impacts modern data architecture.
Explain how you would design a pipeline for streaming real-time order status updates.
Explain how you would optimize a data lake architecture for performance and cost-efficiency
Explain project architecture, technical contributions, and value delivered.
Explain the CAP theorem and its relevance in distributed systems.
Explain why lineage in Spark is crucial for fault tolerance.
Given a problem statement, collaborate with your team to design the entire pipeline architecture.
Handle schema evolution in production.
Handling pipeline bugs
Handling pipeline overload situations
Have you worked with Oozie? If yes, can you explain what it is and how it's used in data pipelines?
High-level ETL Pipeline Design using tools like Kafka or Flink for new use cases?
How do you ensure data quality and consistency in your pipelines?
How do you ensure data quality in a big data pipeline, and what strategies do you use for data validation?
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