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: B, C

Explanation:

These two approaches are recommended by Snowflake for data modeling in a data lake scenario. Creating a raw database allows the data engineering team to ingest data from various sources without any transformation or cleansing, preserving the original data quality and format. This enables the data science team to access the raw data for ML model development. Creating a set of profile-specific databases allows the data engineering team to apply different transformations and optimizations for different use cases and data consumer requirements. For example, the finance and vendor management team can access a dimensional database that supports reporting and visualization, while the sales team can access a secure database that supports data monetization.

Reference: Snowflake Data Lake Architecture | Snowflake Documentation Snowflake Data Lake Best Practices | Snowflake Documentation