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Question-1. What is a data lake?
Answer-1: A centralized repository that stores raw, unprocessed data in its native format, including structured, semi-structured, and unstructured data.
Question-2. What is a data warehouse?
Answer-2: A system designed to store structured data that has been processed, cleaned, and optimized for query and analysis.
Question-3. How do data lakes and data warehouses differ in data storage?
Answer-3: Data lakes store raw data; data warehouses store processed and structured data.
Question-4. Which data types do data lakes support?
Answer-4: Structured, semi-structured, and unstructured data such as logs, videos, images, and JSON files.
Question-5. What type of data does a data warehouse primarily store?
Answer-5: Structured data from transactional systems, cleaned and organized for reporting.
Question-6. How is data processed in a data lake?
Answer-6: Data is ingested in raw form and can be processed later when needed (schema-on-read).
Question-7. How is data processed in a data warehouse?
Answer-7: Data is processed, cleaned, and transformed before storage (schema-on-write).
Question-8. What are the primary use cases for data lakes?
Answer-8: Big data analytics, machine learning, data exploration, and storing diverse data types.
Question-9. What are the primary use cases for data warehouses?
Answer-9: Business intelligence, reporting, and structured data analysis.
Question-10. Which is typically more cost-effective for storing large volumes of data?
Answer-10: Data lakes are usually more cost-effective because they use cheaper storage solutions.
Question-11. What is schema-on-read?
Answer-11: Applying schema to data only when it is read or queried, common in data lakes.
Question-12. What is schema-on-write?
Answer-12: Applying schema to data when it is written into storage, used in data warehouses.
Question-13. Can data warehouses handle unstructured data?
Answer-13: No, they are optimized for structured data.
Question-14. Can data lakes handle structured data?
Answer-14: Yes, they can store all types of data.
Question-15. How do data lakes handle data governance and security?
Answer-15: Data lakes require additional tools and processes for governance and security.
Question-16. How do data warehouses ensure data quality?
Answer-16: By enforcing schema and transformation rules before data storage.
Question-17. Which platform is better suited for machine learning applications?
Answer-17: Data lakes, due to their ability to store large amounts of raw data.
Question-18. How is data accessibility different between data lakes and data warehouses?
Answer-18: Data warehouses provide faster access to cleaned data; data lakes offer flexible but slower access to raw data.
Question-19. What is data cataloging in data lakes?
Answer-19: Organizing and indexing data to improve discoverability and governance.
Question-20. How do data lakes integrate with big data tools?
Answer-20: Data lakes often integrate with Hadoop, Spark, and other big data frameworks.
Question-21. Are data warehouses relational databases?
Answer-21: Yes, they typically use relational database management systems (RDBMS).
Question-22. Do data lakes use relational databases?
Answer-22: No, they use distributed file systems like HDFS or cloud object storage.
Question-23. What is the role of ETL in data warehouses?
Answer-23: Extract, Transform, Load processes clean and structure data before loading into the warehouse.
Question-24. What is ELT in the context of data lakes?
Answer-24: Extract, Load, Transform: raw data is loaded first, then transformed as needed.
Question-25. How do query performances compare?
Answer-25: Data warehouses have faster query performance for structured data due to indexing and optimization.
Question-26. Is data duplication common in data lakes?
Answer-26: Less common, data is stored raw; duplication depends on ingestion strategy.
Question-27. Is data duplication common in data warehouses?
Answer-27: More common because of data transformation and aggregation.
Question-28. Which technology is better for real-time analytics?
Answer-28: Data warehouses often support faster real-time analytics.
Question-29. Can data lakes replace data warehouses?
Answer-29: Data lakes complement but typically do not replace warehouses due to different purposes.
Question-30. What is a lakehouse?
Answer-30: A data architecture combining elements of data lakes and warehouses for flexibility and performance.
Question-31. How do data lakes manage data consistency?
Answer-31: Data lakes have eventual consistency and less strict data governance.
Question-32. How do data warehouses ensure consistency?
Answer-32: They enforce strict ACID properties for reliable transactions.
Question-33. What types of users typically use data lakes?
Answer-33: Data scientists, engineers, and analysts exploring raw data.
Question-34. What types of users use data warehouses?
Answer-34: Business analysts and decision-makers needing reliable reports.
Question-35. Which requires more data preparation before analysis?
Answer-35: Data warehouses require more preparation upfront.
Question-36. How scalable are data lakes?
Answer-36: Highly scalable due to distributed storage and low-cost infrastructure.
Question-37. How scalable are data warehouses?
Answer-37: Scalable but often more expensive and complex than data lakes.
Question-38. What role does metadata play in data lakes?
Answer-38: Metadata is critical for managing and finding data.
Question-39. What role does metadata play in data warehouses?
Answer-39: Used to optimize queries and enforce schema.
Question-40. How do data lakes handle compliance and regulatory requirements?
Answer-40: Through additional governance tools and data classification.
Question-41. Are data warehouses better for compliance?
Answer-41: Yes, because of their structured and governed nature.
Question-42. What cloud services provide data lake solutions?
Answer-42: AWS S3 with Glue, Azure Data Lake Storage, Google Cloud Storage.
Question-43. What cloud services provide data warehouse solutions?
Answer-43: Amazon Redshift, Google BigQuery, Azure Synapse Analytics.
Question-44. What is data wrangling
Answer-44: and where is it done?
Question-45. How do you ensure data quality in data lakes?
Answer-45: By implementing data validation and governance frameworks post-ingestion.
Question-46. Can data lakes support multi-structured analytics?
Answer-46: Yes, they support analytics on all data types.
Question-47. How does cost model differ between the two?
Answer-47: Data lakes have lower storage costs but may have higher processing costs.
Question-48. What challenges do data lakes present?
Answer-48: Data swamp risk, lack of governance, and complex management.
Question-49. What are advantages of data warehouses?
Answer-49: Fast query response, consistent data, and strong governance.
Question-50. When should an organization choose a data lake over a data warehouse?
Answer-50: When dealing with large, diverse datasets for exploratory analytics or machine learning.
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