Explanation:
Redshift streaming ingestion maps Kinesis streams to external schema, uses materialized views with auto-refresh for seconds-level latency and minimal ops. It scales better than JDBC. Flink via JDBC or Firehose to S3 then COPY add latency and complexity. Glue streaming over JDBC has more overhead.
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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