Which solution will meet these requirements?
Use Amazon EC2 instances to ingest and process the data streams to Amazon S3 buckets tor storage. Use Amazon Athena to search the data. Use Amazon Managed Grafana to create visualizations.
Use Amazon EMR to ingest and process the data streams to Amazon Redshift for storage. Use Amazon Redshift Spectrum to search the data. Use Amazon QuickSight to create visualizations.
Use Amazon Elastic Kubernetes Service (Amazon EKS) to ingest and process the data streams to Amazon DynamoDB for storage. Use Amazon CloudWatch to create graphical dashboards to search and visualize the data.
Use Amazon Kinesis Data Streams to ingest and process the data streams to Amazon OpenSearch Service. Use OpenSearch Service to search the data. Use Amazon QuickSight to create visualizations.
Explanations:
While using EC2 for processing and S3 for storage is valid, it lacks a dedicated streaming solution, making it less optimal for real-time updates compared to Kinesis. Athena is not designed for real-time search capabilities.
Amazon EMR can process data, and Redshift is suitable for storage and analytics. However, this solution is not ideal for real-time streaming data ingestion, and Redshift Spectrum is not tailored for immediate data updates. QuickSight is primarily for visualization and not for real-time search.
EKS is a container orchestration service and is not specialized for data streaming. DynamoDB is a NoSQL database that doesn’t support complex queries for analytics in the way that other services do. CloudWatch is for monitoring and logging rather than data searching and visualization.
This option uses Amazon Kinesis Data Streams for real-time data ingestion and processing, which is ideal for streaming applications. The data is stored in Amazon OpenSearch Service, which provides powerful search capabilities. Amazon QuickSight is then used for creating visualizations, aligning well with the requirement for real-time updates and searching capabilities.