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

The correct answer is C. Schema-on-read. Schema-on-read (C) is the defining characteristic of data lake architectures. Data is ingested in its raw or semi-structured form (e.g., JSON, Avro, Parquet stored in VARIANT columns) without requiring a predefined rigid schema at load time. The schema is applied at query time us

Data Engineering

Question

Options

AIDEF1X
BSchema-on-write
CSchema-on-read
DInformation schema

How the community answered

(55 responses)

A
2% (1)
B
5% (3)
C
91% (50)
D
2% (1)

Explanation

Schema-on-read (C) is the defining characteristic of data lake architectures. Data is ingested in its raw or semi-structured form (e.g., JSON, Avro, Parquet stored in VARIANT columns) without requiring a predefined rigid schema at load time. The schema is applied at query time using Snowflake's dot-notation or FLATTEN functions. This makes it ideal for semi-structured and diverse data. Schema-on-write (B) requires defining the schema before loading, which adds friction and is better suited for structured data warehousing. IDEF1X (A) is an entity-relationship diagramming notation, not an ingestion technique. Information Schema (D) is Snowflake's built-in metadata layer, not an ingestion strategy.

Topics

#Semi-structured data#Schema-on-read#Data ingestion#Data lake

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