Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 113 discussion

A. Use AWS DataSync to move the data. Create a custom transformation job by using AWS Glue. - No BW available for DataSync, so "asap" will be weeks/months (?) B. Order an AWS Snowcone device to move the data. Deploy the transformation application to the device. - Snowcone will just store 14TB (SSD configuration). **C**. Order an AWS Snowball Edge Storage Optimized device. Copy the data to the device. Create a custom transformation job by using AWS Glue. - SnowBall can store 80TB (ok), takes around 1 week to move the device (faster than A), and AWS Glue allows to do ETL jobs. This is the answer. D. Order an AWS Snowball Edge Storage Optimized device that includes Amazon EC2 compute. Copy the data to the device. Create a new EC2 instance on AWS to run the transformation application. - Same as C, but the ETL job requires the deployment/configuration/maintenance of an EC2 instance, while Glue is serverless. This means D has more operational overhead than C.

Why C? This answer misses the part between SnowBall and AWS Glue. D at least provides a full-step solution that copies data in snowball device, and installs the custom application in device's EC2 to do the transformation job.

If the data transform runs in the Snowball edge device, does that count as "running on the AWS cloud"? Honest question.

Option Shortcoming A AWS DataSync requires network bandwidth, which is unavailable. B Snowcone’s maximum capacity (14 TB SSD) is insufficient for 50 TB. C AWS Glue would require rewriting the custom application, increasing operational overhead. Option D minimizes operational overhead by leveraging Snowball for offline transfer and EC2 for running the existing application, ensuring a seamless transition to AWS.

One requirement is to run transformation with the data in aws, that is the job of glue

Since the transformation application already there, Snowball Edge devices with compute capability allow to run applications directly on the device. This means the transformation job can start locally on the Snowball Edge device if needed or directly after the data transfer to AWS, minimizing delays. AWS Glue for transformation adds additional refactoring work and operational overhead.

The only way C can work is by using this: Amazon S3 adapter — Use for programmatic data transfer in to and out of AWS using the Amazon S3 API for Snowball Edge, but it is not indicated in the answer. If it includes the step to transfer the data from the Snowball to S3, then C would be a better answer.

Application still need EC2 instance along data transfer with Snowball

Ans D - but I suspect it should be Ans C because, as stated by 123jhlo (1yr 11mth ago), D is not serverless: "**C**. Order an AWS Snowball Edge Storage Optimized device. Copy the data to the device. Create a custom transformation job by using AWS Glue. - SnowBall can store 80TB (ok), takes around 1 week to move the device (faster than A), and AWS Glue allows to do ETL jobs." D. Order an AWS Snowball Edge Storage Optimized device that includes Amazon EC2 compute. Copy the data to the device. Create a new EC2 instance on AWS to run the transformation application. - Same as C, but the ETL job requires the deployment/configuration/maintenance of an EC2 instance, while Glue is serverless. This means D has more operational overhead than C."

A and D are wrong. D is the correct answer. Why not C: AWS Snowball Edge is a physical device designed for transferring large amounts of data to and from AWS. It includes some compute capabilities, such as running AWS Lambda functions, AWS IoT Greengrass, and EC2 instances, but it does not support AWS Glue. AWS Glue is a fully managed ETL (Extract, Transform, Load) service that runs within the AWS Cloud. It is designed to work with data stored in AWS services like Amazon S3, Amazon RDS, and Amazon Redshift, among others. AWS Glue is not available on edge devices like Snowball Edge.

gotta be C surely..... LEAST operational overhead. EC2 = operational overhead. AWS Glue = managed service with transformation capabilities.

The q states that the custom job must remain, so glue is out. Seems like D is the only option as DataSync needs bandwidth.

Using an EC2 instance instead of a managed service like AWS Glue will include more operational overhead for the organization.

The answer is D because of the following key points 1. A custom application in the company’s data center runs a weekly data transformation job. Which means that the company already has an application that runs the transformation. 2. A solutions architect must transfer the data and must configure the transformation job to continue to run in the AWS Cloud. This shows that the only responsibility of the architect is to transfer the data and configure the existing application to run on the EC2 the architect is going to deploy.

A: Cannot be done because no bandwidth B: Snowcone is probably to small D: Doable by EC2 is overhead for transformation when Glue is an option C: Is correct as Snowball Edge Storage Optimised device is good for storage and Glue can transform once the data is available

C best suit for: ETL jobs with LEAST operational overhead. For my understanding, we need here to avoir operation or maintenance burden of the solution

we use snowball to copy 50 PB "The company plans to pause the application until the data transfer is complete " and least over head " hence C would be reinventing the weel