Explanation:
The correct choice is Amazon Redshift streaming ingestion with external schema on Kinesis, materialized view, auto refresh. Redshift’s native streaming ingestion maps Kinesis Data Streams into an external schema and populates a materialized view that can be auto-refreshed, delivering seconds-level latency with minimal operational overhead. It scales far better than JDBC-based sinks and avoids staging complexity.
Ultimate access to all questions.
How should a company ingest about 5 GB/s from Amazon Kinesis Data Streams into Amazon Redshift with seconds-level latency and minimal operations for near-real-time BI dashboards?
A
Amazon Kinesis Data Analytics (Flink) writing to Amazon Redshift via JDBC
B
Amazon Kinesis Data Firehose to Amazon S3 then Amazon Redshift COPY
C
Amazon Redshift streaming ingestion with external schema on Kinesis, materialized view, auto refresh
D
AWS Glue streaming job to Amazon Redshift over JDBC
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