Topic 1 Question 320
A company is using a fleet of Amazon EC2 instances to ingest data from on-premises data sources. The data is in JSON format and ingestion rates can be as high as 1 MB/s. When an EC2 instance is rebooted, the data in-flight is lost. The company’s data science team wants to query ingested data in near-real time.
Which solution provides near-real-time data querying that is scalable with minimal data loss?
Publish data to Amazon Kinesis Data Streams, Use Kinesis Data Analytics to query the data.
Publish data to Amazon Kinesis Data Firehose with Amazon Redshift as the destination. Use Amazon Redshift to query the data.
Store ingested data in an EC2 instance store. Publish data to Amazon Kinesis Data Firehose with Amazon S3 as the destination. Use Amazon Athena to query the data.
Store ingested data in an Amazon Elastic Block Store (Amazon EBS) volume. Publish data to Amazon ElastiCache for Redis. Subscribe to the Redis channel to query the data.
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コメント(6)
- 正解だと思う選択肢: A
A: is the solution for the company's requirements. Publishing data to Amazon Kinesis Data Streams can support ingestion rates as high as 1 MB/s and provide real-time data processing. Kinesis Data Analytics can query the ingested data in real-time with low latency, and the solution can scale as needed to accommodate increases in ingestion rates or querying needs. This solution also ensures minimal data loss in the event of an EC2 instance reboot since Kinesis Data Streams has a persistent data store for up to 7 days by default.
👍 6LuckyAro2023/02/21 near-real-time data querying = Kinesis analytics
👍 2cloudbusting2023/02/18- 正解だと思う選択肢: A
• Provide near-real-time data ingestion into Kinesis Data Streams with the ability to handle the 1 MB/s ingestion rate. Data would be stored redundantly across shards. • Enable near-real-time querying of the data using Kinesis Data Analytics. SQL queries can be run directly against the Kinesis data stream. • Minimize data loss since data is replicated across shards. If an EC2 instance is rebooted, the data stream is still accessible. • Scale seamlessly to handle varying ingestion and query rates.
👍 2kruasan2023/04/29
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