180 ) Best practices

 

1. Best practices of data modeling✅
2. Best practices of SQL query✅
   

179 ) Project based qsns

 

1. Tell me about your background and exp
2.  Tell me about your Data modeling projects
1. Insurance project ✅
2. Banking project
3. Food delivery project 
4. Waste management project 
5. Hospital project 
6. pharma 
7. Sample - Data model Diagrams

178 ) Cloud qsns

 

1. AWS qsns
1. Aws solution architect project qsns✅
2. AWS interview qsns✅
3.  ) AWS : which features did you work 
   
   
2. Azure qsns
1. Azure solution architect project qsns✅
2. Which Azure features and project did you work on 
3. Azure data bricks 
4. Azure qsn flow 

177 ) Performance tuning qsns

 

1. Reasons for slow running query 
2. how to tune slow query - Performance tuning questions ..
1. sql server✅
2. oracle ✅
3. mysql✅

176 ) Warehouse concepts ( pipeline , lakehouse , vault , governance, mesh)

 

1. 133 ) Compare DWH tools - Snowflake, synapse , bigquery, Redshift
2. 134 ) when should customer prefer  -  dwh tools 
1. snowflake ,
2.  Azure synapse ,
3.  GCP bigquery, 
4. aws redshift
3. Data mesh✅
4. Data vault
1. did you work on Data vault project  ✅
2. How did you handle the changes in data model  ✅
3. Datavault faqs
5. Data pipeline✅
1. data pipeline is a set of automated scripts,   that move data from one or more source systems, transform it   and load it into a destination target (such as a data warehouse, or lake house)
6. Data lineage✅ 
1. (tracking data from source to destination in diagram )
7. Data lake    ✅
1. Data Lake is a centralized, highly scalable storage repository that holds a vast amount of raw, unprocessed data in its native format (structured, semi-structured, and unstructured) until it is needed for analytics
8. Data lakehouse and how to design ( DATA BRICKS )✅
   
   
9. Diff between Datalake and Data lakehouse


10. Data warehouse✅
11. Data governance  ✅

1. Data governance is a collection of policies, procedures, standards, roles, and responsibilities that collectively ensure an organization's data is accurate, secure, compliant, and effectively managed throughout its entire lifecycle.

2. Data Governance FAQS
3. HIPAA , GDPR 
12. How to do Data profiling✅
13. ETL FAQ questions
14. ETL & ELT ( when to suggest )
15. Did you work on NO SQL databased AND Cassandra DB

175 ) Challenges you faced in DM projects

 

1. List of challenges 
2. Banking DM project Challenges : ✅
3. insurance DM project Challenges  ✅
4. food delivery DM project Challenges ✅
5. pharma DM project Challenges 

174 ) Dimensional modeling questions

 

1. Why do we need Data Warehouse
2. Enterprise data modeling - Insurance project OLTP
3. Types of data models 
4. star or snowflake schemas ( how to chose)
5. Types of facts and dimensions
6. OLTP & OLAP diff
7. SCD types and implementation
8. Experience in Metadata management MDM
9. Data dictionary✅
10. STTM