A gaming company is developing a real-time multiplayer gaming analytics platform that receives gameplay telemetry from active concurrent players. The telemetry data stream is sent in JSON format, averaging with peak volumes reaching during global events. The platform must ingest this telemetry with sub-second latency, perform stateful anomaly detection and metrics aggregation over a sliding window of , and save the transformed output in Apache Parquet format to Amazon S3 for long-term historical query access. The architecture must scale dynamically to handle peak volumes without manual intervention or administrative overhead.
Which combination of actions will meet these requirements? (Select two.)
- Configure an Amazon Kinesis Data Stream with on-demand capacity mode to ingest the incoming gaming telemetry.Cevap
- Deploy an Amazon Managed Service for Apache Flink application to process the telemetry stream, calculate sliding-window metrics, and output to an Amazon Data Firehose delivery stream for Parquet conversion and delivery to Amazon S3.Cevap
- CConfigure an Amazon Kinesis Data Stream with provisioned capacity mode set to shards to ingest the telemetry, and use Kinesis Client Library applications on Amazon EC2 to calculate the metrics.
- DConfigure an Amazon SQS standard queue to ingest the telemetry, and run containerized consumer applications on AWS Fargate to compute the sliding-window metrics.
- EConfigure an Amazon Data Firehose delivery stream with a buffer interval of to ingest the telemetry, perform the sliding-window aggregation using an inline AWS Lambda function, and deliver the data directly to Amazon S3.
Cevap
To meet the requirements, the solutions architect should configure an Amazon Kinesis Data Stream in on-demand capacity mode for the ingestion layer, and deploy an Amazon Managed Service for Apache Flink application for stateful sliding-window processing, sending the output to Amazon Data Firehose for format conversion and delivery to Amazon S3.
The correct solution involves configuring Amazon Kinesis Data Streams in on-demand mode and using Amazon Managed Service for Apache Flink with Amazon Data Firehose. Kinesis Data Streams in on-demand mode scales dynamically to handle write throughput up to and records per second, which comfortably supports the peak throughput of without manual administrative overhead. Amazon Managed Service for Apache Flink provides a fully managed, serverless platform for stateful stream processing, making it ideal for performing computations like -minute sliding-window aggregations. By routing the output of Flink to Amazon Data Firehose, the platform can utilize Firehose's native integration with AWS Glue Schema Registry to convert the JSON data to Apache Parquet format before writing it to Amazon S3, satisfying all performance, latency, and operational requirements.
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Anahtar Kavram
Serverless high-throughput real-time stream ingestion and stateful time-window transformation using Kinesis Data Streams, Managed Service for Apache Flink, and Amazon Data Firehose.