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by ta3411
1376 days ago
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This seems like an ideal use case for us. I have a naive thinking of my workflow: can someone please comment if I am off track. I am building an e-commerce product on AWS PostgresSQL. Everyday, I want to be able to do analytics on order volume, new customers, etc.
- For us to track internally: we fire client and backend events into Amplitude
- For sellers to track: we directly query PostgressQL to export Now with this, I am thinking of constantly streaming our SQL table to BigQuery. And any analysis can be done on top of this BigQuery instance across both internal tracking and external export. Is RedShift the AWS equivalent of this? |
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That said the closest thing is Amazon Athena.
The architecture would basically be Kinesis -> S3 <- Athena where S3 is your data lake or you can do it like AWS DMS -> S3 <- Athena.
To accomplish this or the redshift solution you need to implement change data capture from your relational DB, for that you can sue AWS Database Migration Service like this for redshift: https://aws.amazon.com/blogs/apn/change-data-capture-from-on...
Like this for kinesis: https://aws.amazon.com/blogs/big-data/stream-change-data-to-...
The reason you may want to use Kinesis is because you can use Flink in Kinesis Data Analytics just like you can use DataFlow in GCP to aggregate some metrics before dumping them into your data lake/warehouse.