A company needs to ingest data from Amazon S3, Amazon Redshift, and Snowflake into Amazon SageMaker Data Wrangler. The ingested data must always reflect the latest changes in the source systems. Which approach meets this requirement?
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Correct answer: Use cataloged connections to import data from the sources into Data Wrangler..
Why this is the answer
Cataloged connections in Data Wrangler, leveraging AWS Glue Data Catalog, are the correct choice because they provide a persistent, up-to-date metadata store for your data sources. When you import data using a cataloged connection, Data Wrangler refers to the Glue Data Catalog, which can be regularly updated (e.g., via Glue crawlers) to reflect the latest schema and data changes in S3, Redshift, and Snowflake. This ensures that Data Wrangler always accesses the most current version of your data. Direct connections are for one-time or ad-hoc imports and don't automatically reflect ongoing changes. Using AWS Glue or Lambda to extract and then import data directly into Data Wrangler would involve an unnecessary intermediate step and wouldn't inherently guarantee that Data Wrangler's view is always current without additional orchestration.
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