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BigData / Data Lake Interview questions

What data modeling approaches work best for Data Lakes?

Data lake modeling balances flexibility with structure. Raw zones use schema-on-read with minimal modeling. Refined zones apply dimensional modeling (star/snowflake schemas), data vault (hub/link/satellite for auditability), or denormalized wide tables. Modern lakehouses blend approaches: raw Bronze, dimensional Silver, denormalized Gold. Key considerations: query patterns drive model design, avoid premature optimization, support schema evolution, maintain business meaning. Tools: dbt for transformations, Delta/Iceberg for schema enforcement.

What modeling approach uses hub, link, and satellite tables?
Which layer typically uses dimensional modeling?

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What is a Data Lake? Explain the Bronze, Silver, and Gold layer architecture in Data Lakes? What are the key differences between a Data Lake and a Data Warehouse? Explain Schema-on-Read vs Schema-on-Write approaches in data management? Compare cloud storage platforms for Data Lakes: Amazon S3, Azure Data Lake Storage, and Hadoop HDFS? What is a Data Lakehouse and how does it differ from traditional Data Lakes? What is Delta Lake and what features does it provide? What is Apache Iceberg and how does it improve Data Lake table management? What is Apache Hudi and what capabilities does it provide for Data Lakes? How can organizations prevent Data Lakes from becoming Data Swamps? What are effective data partitioning strategies in Data Lakes? What file formats are best suited for Data Lakes and why? Explain different data ingestion patterns for Data Lakes? What is Lambda Architecture and how does it relate to Data Lakes? What is Kappa Architecture and when should it be used? What are Data Cataloging tools and how do they help manage Data Lakes? How do you implement security and access control in Data Lakes? Explain data versioning and time travel capabilities in Data Lakes? What is the difference between ETL and ELT in the context of Data Lakes? How do you implement Data Governance in a Data Lake? What are data quality best practices for Data Lakes? How do you handle streaming data in Data Lakes? What is metadata management and why is it critical for Data Lakes? What are cost optimization strategies for cloud-based Data Lakes? How do you implement data retention and lifecycle policies in Data Lakes? What monitoring and observability practices should be implemented for Data Lakes? How do you implement backup and disaster recovery for Data Lakes? What is data compaction and why is it important in Data Lakes? What query engines work with Data Lakes (Presto, Athena, Spark SQL)? How do you tune Data Lake query performance? What are Data Lake scalability considerations? How do Data Lakes integrate with other systems? What data modeling approaches work best for Data Lakes? How do you integrate Machine Learning with Data Lakes? How do you ensure compliance (GDPR, CCPA, HIPAA) in Data Lakes? What are Data Lake migration strategies from on-premises to cloud? What testing strategies should be used for Data Lake pipelines? What documentation practices are essential for Data Lakes? What are emerging trends and the future of Data Lake technology? What are real-world Data Lake use cases and best practices?
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