Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 557 discussion

Option B is the correct solution that meets the requirements: Use Amazon EMR to process the semi-structured data in Amazon S3. EMR provides a managed Hadoop framework optimized for processing large datasets in S3. EMR supports parallel data processing across multiple nodes to speed up the processing. EMR can integrate directly with Amazon Redshift using the EMR-Redshift integration. This allows querying the Redshift data from EMR and joining it with the S3 data. This enables enriching the semi-structured S3 data with the information stored in Redshift

By combining AWS Glue and Amazon Redshift, you can process the semistructured data in parallel using Glue ETL jobs and then store the processed and enriched data in a structured format in Amazon Redshift. This approach allows you to perform complex analytics efficiently and at scale.

D: not relevant, data is semistructured and Glue is more batch than stream data A: not correct, Athena is for querying data B & C look ok but C is out => redundant with Kinesis data stream; EMR already processed data as input into Redshift for parallel processing Only B is most logical

Key requirement: parallel data processing parallel data processing is EMR (Kind of Apache Hadoop) so it only leave B and C C is Kinesis to Redshift which is pointless logic here B EMR for S3 and EMR for Redshift gives maximum parallel processing here

A has a pitfall, "use Amazon Athena to PROCESS the data". With Athena you can query, not process, data. C is wrong because Kinesis has no place here. D is wrong because it does not process the Redshift data, and Glue does ETL, not analyze Thus it's B. EMR can use semi-structured data from from S3 and structured data from Redshift and is ideal for "parallel data processing" of "large amounts" of data.

large amount of data + parallel data processing = EMR

Amazon Athena is an interactive query service that makes it easy to analyze data directly in Amazon Simple Storage Service (Amazon S3) using standard SQL.

Selected Answer: D Glue use apache pyspark cluster for parallel processing. EMR or Glue are possible options. Glue is serverless so better use this plus pyspark is in memory parallel processing.

i think a is correct semistructured data ==> Athena

Athena is not designed for parallel data processing. So it's B

Answer is A

From this documentation looks like EMR cannot interface with S3. https://aws.amazon.com/emr/ I will settle with option A.

For those answering A, AWS Glue can directly query S3, it can't use Athena as a source of data. The questions say the Redshift data should be user to "enrich" which means thats the redshift data needs to be "added" to the s3 data. A doesn't allow that.

Choose option B. Option A is not correct. Amazon Athena is suitable for querying data directly from S3 using SQL and allows parallel processing of S3 data. AWS Glue can be used for data preparation and enrichment but might not directly integrate with Amazon Redshift for enrichment.

Athena and Redshift both do SQL query

semi-structure supported by Athena not by EMR

athena for s3