MLS-C01 · Question #19
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data. The ingestion process must buffer and convert incoming records from JSON…
The correct answer is A. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon. Use AWS Glue to create a schema in the AWS Glue Data Catalog. Kinesis Data Firehose then https://docs.aws.amazon.com/firehose/latest/dev/record-format-conversion.html https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon…
Question
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data. The ingestion process must buffer and convert incoming records from JSON to a query- optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards. Which solution should the Data Scientist build to satisfy the requirements?
Options
- ACreate a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon
- BWrite each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an
- CWrite each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an
- DUse Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL
How the community answered
(44 responses)- A45% (20)
- B34% (15)
- C11% (5)
- D9% (4)
Explanation
Use AWS Glue to create a schema in the AWS Glue Data Catalog. Kinesis Data Firehose then https://docs.aws.amazon.com/firehose/latest/dev/record-format-conversion.html https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon- kinesis-data-firehose-amazon-athena-and-amazon-redshift/
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