@APentyala Please find the answers below: 1. Which data modeling approach is recommended for this domain? Ans: If you have large data, we would recommend to go with Partitioning or multi-level partitioning. You could implement Bucketing if the data inside partition is large. 2. Are there any sample models available for reference? Ans: You could take a refrence for partitioning and bucketing in https://www.linkedin.com/pulse/what-partitioning-vs-bucketing-apache-hive-shrivastava/ You could create a new table perfroom CTAS with Dynamic Partitiining from the existing table Refrence: https://www.geeksforgeeks.org/overview-of-dynamic-partition-in-hive/ 3. What best practices should we follow to ensure data integrity and performance? Ans: Please follow below best parctices: a. Paartion and bucket it b. You could use Iceberg table which would reduce the significant load on Metastore, if you are using CDP Public CLoud or CDP private CLoud(ECS/Opesnshit) c. Use ORC/parquet d. Use EXTERNAL tables,if you dont perfrom Update/Delete as reading External table is faster. 4. How can we efficiently manage large-scale data ingestion and processing? Ans: The model follows as: Kafka/Spark Streaming: Ingestion Spark: Data Modelling Hive: Warehosuing where you extract the data Please. be specific on the use case. 5. Are there any specific challenges or pitfalls we should be aware of when implementing a lakehouse in this sector? Ans: There should be no challenges, we would request to provide more briefing on this.
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