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1.
What is data modeling and why is it important?
Answer:
Data Modeling and Database Design
- 2.
Explain the difference between conceptual, logical, and physical data models.
Answer: - 3.
What are the key steps in the data modeling process?
Answer: - 4.
Describe the different types of relationships in a relational database.
Answer: - 5.
What is normalization and why is it used in database design?
Answer: - 6.
Explain the difference between OLTP and OLAP systems.
Answer: - 7.
What is a star schema and when would you use it?
Answer: - 8.
Describe the concept of slowly changing dimensions (SCDs) in data warehousing.
Answer: - 9.
What is a fact table and how does it differ from a dimension table?
Answer: - 10.
Explain the purpose of surrogate keys in data modeling.
Answer:
Data Warehousing and ETL
- 11.
What is a data warehouse and its key characteristics?
Answer: - 12.
Explain the ETL (Extract, Transform, Load) process and its stages.
Answer: - 13.
What are the common challenges faced during ETL processes?
Answer: - 14.
Describe the difference between full load and incremental load in ETL.
Answer: - 15.
What is data staging and why is it important in ETL?
Answer: - 16.
Explain the concept of data lineage and its significance in data warehousing.
Answer: - 17.
What are the benefits of using a data warehouse?
Answer: - 18.
Describe the role of data quality in ETL processes.
Answer: - 19.
What is a slowly changing dimension (SCD) and how is it handled in ETL?
Answer: - 20.
Explain the difference between a data warehouse and a data mart.
Answer:
Big Data Technologies
- 21.
What is Hadoop and its core components?
Answer: - 22.
Explain the difference between Hadoop and Spark.
Answer: - 23.
What is MapReduce and how does it work?
Answer: - 24.
Describe the role of HDFS in Hadoop.
Answer: - 25.
What is Hive and how is it used in big data processing?
Answer: - 26.
Explain the concept of data partitioning in Hadoop.
Answer: - 27.
What is Kafka and its use cases in data engineering?
Answer: - 28.
Describe the difference between batch processing and stream processing.
Answer: - 29.
What is Cassandra and its key features?
Answer: - 30.
Explain the concept of data replication in Hadoop.
Answer:
Data Processing and Transformation
- 31.
What is data processing and its stages?
Answer: - 32.
Explain the difference between batch processing and real-time processing.
Answer: - 33.
What are the common data transformation techniques?
Answer: - 34.
Describe the role of data cleansing in data processing.
Answer: - 35.
What is data enrichment and why is it important?
Answer: - 36.
Explain the concept of data aggregation and its use cases.
Answer: - 37.
What is data deduplication and how is it achieved?
Answer: - 38.
Describe the difference between data filtering and data sorting.
Answer: - 39.
What is data normalization and its techniques?
Answer: - 40.
Explain the purpose of data validation in data processing.
Answer:
Data Integration and Pipelines
- 41.
What is data integration and its challenges?
Answer: - 42.
Explain the difference between ETL and ELT approaches.
Answer: - 43.
What are the common data integration patterns?
Answer: - 44.
Describe the role of data pipelines in data engineering.
Answer: - 45.
What is a data lake and how does it differ from a data warehouse?
Answer: - 46.
Explain the concept of data ingestion and its methods.
Answer: - 47.
What is change data capture (CDC) and its use cases?
Answer: - 48.
Describe the difference between batch and streaming data integration.
Answer: - 49.
What is data replication and its techniques?
Answer: - 50.
Explain the purpose of data orchestration in data pipelines.
Answer:
Data Storage and Retrieval
- 51.
What is a database management system (DBMS) and its types?
Answer: - 52.
Explain the difference between SQL and NoSQL databases.
Answer: - 53.
What is data partitioning and its strategies?
Answer: - 54.
Describe the concept of data indexing and its benefits.
Answer: - 55.
What is data sharding and when is it used?
Answer: - 56.
Explain the difference between vertical and horizontal scaling in databases.
Answer: - 57.
What is data replication and its types?
Answer: - 58.
Describe the role of caching in data retrieval.
Answer: - 59.
What is a data lake and its architecture?
Answer: - 60.
Explain the concept of data archiving and its importance.
Answer:
Data Governance and Security
- 61.
What is data governance and its key components?
Answer: - 62.
Explain the difference between data governance and data management.
Answer: - 63.
What are the common data governance frameworks?
Answer: - 64.
Describe the role of data lineage in data governance.
Answer: - 65.
What is data quality and its dimensions?
Answer: - 66.
Explain the concept of data stewardship and its responsibilities.
Answer: - 67.
What is data security and its best practices?
Answer: - 68.
Describe the difference between authentication and authorization in data security.
Answer: - 69.
What is data encryption and its types?
Answer: - 70.
Explain the purpose of data auditing and its techniques.
Answer:
Data Monitoring and Optimization
- 71.
What is data monitoring and its importance?
Answer: - 72.
Explain the difference between real-time and batch monitoring.
Answer: - 73.
What are the common data monitoring tools and techniques?
Answer: - 74.
Describe the role of data profiling in data monitoring.
Answer: - 75.
What is data optimization and its strategies?
Answer: - 76.
Explain the concept of data partitioning and its benefits in optimization.
Answer: - 77.
What is query optimization and its techniques?
Answer: - 78.
Describe the difference between data compression and data deduplication.
Answer: - 79.
What is data caching and its use cases in optimization?
Answer: - 80.
Explain the purpose of data archiving in data optimization.
Answer:
Data Engineering Tools and Frameworks
- 81.
What is Apache Spark and its key features?
Answer: - 82.
Explain the difference between Spark RDDs and DataFrames.
Answer: - 83.
What is Apache Airflow and its use cases?
Answer: - 84.
Describe the role of Apache Kafka in data streaming.
Answer: - 85.
What is Talend and its key components?
Answer: - 86.
Explain the concept of data pipelines in Apache NiFi.
Answer: - 87.
What is Informatica PowerCenter and its features?
Answer: - 88.
Describe the difference between Hadoop and Apache Flink.
Answer: - 89.
What is dbt (Data Build Tool) and its benefits?
Answer: - 90.
Explain the purpose of Presto in data querying.
Answer:
Cloud Data Engineering
- 91.
What is cloud data engineering and its advantages?
Answer: - 92.
Explain the difference between AWS, Azure, and Google Cloud Platform for data engineering.
Answer: - 93.
What is Amazon S3 and its use cases in data storage?
Answer: - 94.
Describe the role of Azure Data Factory in data integration.
Answer: - 95.
What is Google BigQuery and its key features?
Answer: - 96.
Explain the concept of serverless data processing in AWS Lambda.
Answer: - 97.
What is Azure Databricks and its benefits?
Answer: - 98.
Describe the difference between Amazon Redshift and Google BigQuery.
Answer: - 99.
What is AWS Glue and its use cases in data integration?
Answer: - 100.
Explain the purpose of Google Cloud Dataflow in data processing.
Answer:
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