Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?

Option C is the correct answer because schema-on-read is a technique that allows Snowflake to

ingest and consume semi-structured data without requiring a predefined schema. Snowflake

supports various semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML, and

provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides

native support for querying semi-structured data using SQL and dot notation. Schema-on-read

enables Snowflake to query semi-structured data at the same speed as performing relational queries

while preserving the flexibility of schema-on-read. Snowflake’s near-instant elasticity rightsizes

compute resources, and consumption-based pricing ensures you only pay for what you use.

Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and

constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and

consuming semi-structured data, which does not have a fixed schema or structure.

Option B is incorrect because schema-on-write is a technique that requires defining a schema before

loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-

structured data, which may have varying or complex structures that are difficult to fit into a

predefined schema. Schema-on-write also introduces additional overhead and complexity for data

transformation and validation.

Option D is incorrect because information schema is a set of metadata views that provide

information about the objects and privileges in a Snowflake database. Information schema is not a

technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata

about the data.

Reference:

Semi-structured Data

Snowflake for Data Lake