What should a solutions architect recommend to meet these requirements?
Use Amazon Redshift Spectrum to query the data in Amazon S3 directly and join that data with the existing data in Amazon Redshift. Use Amazon QuickSight to build the visualizations.
Use Amazon Athena to query the data in Amazon S3. Use Amazon QuickSight to join the data from Athena with the existing data in Amazon Redshift and to build the visualizations.
Increase the size of the Amazon Redshift cluster, and load the data from Amazon S3. Use Amazon EMR Notebooks to query the data and build the visualizations in Amazon Redshift.
Export the data from the Amazon Redshift cluster into Apache Parquet files in Amazon S3. Use Amazon Elasticsearch Service (Amazon ES) to query the data. Use Kibana to visualize the results.
Explanations:
Amazon Redshift Spectrum allows querying of data directly from Amazon S3 without needing to load it into Redshift. This solution enables efficient joining of the new data with existing data in Redshift. Coupled with Amazon QuickSight for visualization, it provides a low-cost and low-effort method for reporting.
While Amazon Athena can query data in S3, it does not support joining data directly with existing data in Amazon Redshift. The integration with QuickSight for visualizations is valid, but the lack of a direct join with Redshift makes this option less optimal compared to Option A.
Increasing the size of the Amazon Redshift cluster and loading data from S3 incurs higher costs and management overhead. Although it enables data querying within Redshift, it is not the most efficient or cost-effective solution given the data size. Using Amazon EMR Notebooks is an extra step that complicates the process.
Exporting data to Apache Parquet files and using Amazon Elasticsearch Service adds unnecessary complexity and cost. It requires additional data transformation and management, which is not ideal for quick reporting and visualization needs. Kibana is not as directly integrated for the data sources in this scenario as QuickSight is with Redshift and S3.
I systematize that the answer is:
Use Amazon Redshift Spectrum to query the data in Amazon S3 directly and join that data with the existing data in Amazon Redshift. Use Amazon QuickSight to build the visualizations.