A company is using Snowflake in Azure in the Netherlands. The company analyst team also has data in JSON format that is stored in an Amazon S3 bucket in the AWS Singapore region that the team wants to analyze.
The Architect has been given the following requirements:
1. Provide access to frequently changing data
2. Keep egress costs to a minimum
3. Maintain low latency
How can these requirements be met with the LEAST amount of operational overhead?
I think it's A. It would cover the requirements closely in my opinion. https://docs.snowflake.com/en/user-guide/tables-external-intro
Pros: Collocating data in Azure Blob storage minimizes egress costs and latency. Snowpipe automates the data ingestion process, reducing operational overhead. Cons: Initial data transfer will have egress costs, but once collocated, costs will be minimized. Reasoning: This option balances the need for real-time or near-real-time data access, low latency, and minimized egress costs with the least operational overhead. • Data Collocation: Once the data is in Azure Blob storage, accessing it from Snowflake in Azure Netherlands will not incur additional egress costs and will have low latency. • Automated Ingestion: Using Snowpipe automates the ingestion process, reducing the need for manual intervention and continuous management. • Operational Overhead: Compared to managing replication and external tables across clouds, this approach requires less operational effort.
Answer is C based on following analysis
Option C involves copying data between providers from AWS S3 to Azure Blob storage and then using Snowpipe for data ingestion. This approach can be labor-intensive and might incur higher egress costs due to data transfer between different cloud providers. Additionally, it might lead to higher operational overhead since you'll need to manage and monitor the data transfer process and the Snowpipe setup. In contrast, Option D involves using the AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and Azure Netherlands Blob storage. This approach automates the replication process, reduces operational overhead, and keeps egress costs low by maintaining data within the same cloud regions