Which component is NOT typically used in building continuous ELT pipelines?

• A. Snowpipe
• B. Snowflake Connector for Kafka
• C. Data Exchange
• D. Streams

Answer: (C)

Continuous ELT pipelines rely on mechanisms that automatically ingest and surface data into Snowflake as soon as it arrives, while also enabling incremental, change-driven loading. Snowpipe fits this need by loading files from a stage into tables automatically when new data lands, eliminating manual batch loading. The Snowflake Connector for Kafka supports real-time or near-real-time ingestion of streaming data from Kafka topics, keeping Snowflake up to date with the latest events. Snowflake Streams provides change data capture on a table, letting you identify and process only new or changed rows for downstream transformations, which is essential for efficient incremental loading in ELT flows. Data Exchange, on the other hand, is designed for sharing data between Snowflake accounts or organizations. It’s a data-sharing mechanism rather than a component that handles ingestion, streaming, or change-tracking within a pipeline. So while Data Exchange is valuable for distributing or consuming datasets, it doesn’t play a typical role in building continuous ELT pipelines, making it the correct choice for this question.