DEA-C01 Data Store Management Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Use the Amazon Redshift Streaming Ingestion feature to directly ingest from Kinesis Data Streams into Redshift materialized views.

  Why this is correct

  Redshift Streaming Ingestion allows you to create materialized views that directly consume from Kinesis Data Streams, providing low-latency ingestion without intermediate storage. This reduces load on the cluster and enables near-real-time analytics by querying the materialized views, which can be refreshed automatically.

• ✗

  Use Amazon Kinesis Client Library (KCL) on an Amazon EC2 instance to read from Kinesis and insert data into Redshift using INSERT statements.

  Why it's wrong here

  Using KCL on EC2 requires managing infrastructure and writing custom code. INSERT statements are inefficient for bulk data loads and can degrade query performance. This approach is not scalable and lacks the optimizations of Redshift Streaming Ingestion.

• ✗

  Use Amazon Kinesis Data Firehose to deliver the stream to an Amazon S3 bucket, then use a COPY command to load data into Redshift.

  Why it's wrong here

  This approach introduces latency because data must be buffered in S3 before loading. It also requires managing the COPY process, which may not provide near-real-time ingestion. While it is a common batch pattern, it does not meet the near-real-time requirement as effectively as a direct streaming ingestion method.

• ✗

  Use AWS Glue streaming ETL to read from Kinesis and write to Redshift using JDBC connections.

  Why it's wrong here

  AWS Glue streaming ETL can process data, but writing to Redshift via JDBC is not optimized for high-throughput ingestion and can impact cluster performance. It also adds complexity and latency. Redshift Streaming Ingestion is purpose-built for this use case and integrates natively.

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