3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements?

The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:

1) Finance and Vendor Management team members who require reporting and visualization

2) Data Science team members who require access to raw data for ML model development

3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)
A . Consolidate data in the company’s data lake and use EXTERNAL TABLES.
B . Create a raw database for landing and persisting raw data entering the data pipelines.
C . Create a set of profile-specific databases that aligns data with usage patterns.
D . Create a single star schema in a single database to support all consumers’ requirements.
E . Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

View Answer

Answer: BC

Explanation:

To accommodate the diverse needs of different teams and use cases within a company, a flexible and multi-faceted approach to data modeling is required.

Option B: By creating a raw database for landing and persisting raw data, you ensure that the Data Science team has access to unprocessed data for machine learning model development. This aligns with the best practices of having a staging area or raw data zone in a modern data architecture where raw data is ingested before being transformed or processed for different use cases.

Option C: Having profile-specific databases means creating targeted databases that are designed to meet the specific requirements of each user profile or team within the company. For the Finance and Vendor Management teams, the data can be structured and optimized for reporting and visualization. For the Sales team, the database can include engineered and protected data that is suitable for data monetization efforts. This strategy not only aligns data with usage patterns but also helps in managing data access and security policies effectively.