Welcome to the exam cram for to migration and transfer services. Getting started with the Server Migration
Service, SMS.
This is an agentless
service for migrating on-premises and cloud-based VMs to AWS. Source platforms can be VMware, Hyper-V
or Azure. The Server Migration Service connector is installed on the source's platform.
Server volumes are replicated and it's encrypted with TLS and saved as AMIs, which can then be launched
as EC2 instances.
You can use application groupings and SMS will launch servers in a CloudFormation stack. And
you can replicate your on-premises service to AWS for up to 90 days per server.
It provides automated live incremental server replication and AWS console support.
The Database Migration Service is used for migrating databases from on-premises, Amazon EC2 or Amazon
RDS.
It supports homogeneous, so Oracle to Oracle, as well as heterogeneous, example, Oracle to Amazon.
Aurora. Data is continuously replicated while the application is live, minimizing downtime.
You pay based on the compute resources used during the migration and the log storage.
It's a fully managed migration process. And you can use it along with the schema conversion tool for
converting schemas.
DMS use cases include cloud to cloud, so EC2 to RDS, RDS to RDS, or RDS to Aurora,
on premises to cloud and homogeneous migrations like Oracle to Oracle, MySQL to RDS MySQL, and
so on.
Also, heterogeneous migration.
So, you might be moving from Oracle to Aurora, Oracle to Postgres, or Microsoft SQL to RDS MySQL.
And then you'll need to use the schema conversion tool.
It's also useful for development and test, so you can use the cloud for dev/test workloads and database
consolidation.
So, consolidating multiple source DBs to a single target DB.
It also does continuous data replication so you can use it for DR, dev/test, or single source multi target
or multi-source single target.
Moving on to DataSync. This has a software agent to connect to on-premises NAS storage systems.
The NAS can use NFS or SMB protocols, and it synchronizes data into AWS using a scheduled automated
data transfer with TLS encryption. Destinations can be S3, EFS, or FSx for Windows File Server. And
it can improve performance for data transfers.
Permissions and metadata are preserved. And you pay per gigabyte transferred.
We've then got the Snowball family.
These are used for migrating large volumes of data to AWS.
The Snowball Edge Compute Optimized provides block and object storage and an optional GPU.
You can use up the data collection, machine learning and processing and storage in environments with
intermittent connectivity, so edge use cases.
We then have the Snowball Edge Storage Optimized.
This provides block storage and S3 compatible object storage.
You can use it for local storage and large scale data transfer.
Then we've got Snowcone.
This is a small device used for edge computing storage and data transfer.
You can transfer data offline or online with the AWS DataSync agent.
Snowball uses secure storage devices for physical transportation, and you have some client software
that you install on a local machine and that can be used to identify the data that you want to transfer,
compress it, encrypt it, and then actually transfer it.
Snowball uses 256-bit encryption with managed KMS keys and tamper resistant enclosures with TPM.
We then have the different devices. So, Snowball is capable of 80 terabytes or 50 terabytes depending
on your model, and that's petabyte scale.
Snowball Edge goes up to 100 terabytes and that's also known as petabyte scale.
We then have the Snowmobile.
This is known as being exabyte scalable up to 100 petabytes per Snowmobile.
There are a few ways to optimize the performance of Snowball transfers.
You can use the latest Mac or Linux Snowball client, batch small files together, perform multiple copy
operations at one time,
copy from multiple workstations, and transfer directories, not individual files.
Use cases for Snowball are cloud data migration,
so migrating data to the cloud. Content distribution,
sending data to clients or customers.
Tactical edge computing, so you're
collecting data and computing it somewhere at the edge.
Machine learning, run
machine learning directly on the
devices themselves, and manufacturing, so data collection and analysis in the factory.
And it's useful for remote locations with simple data. You can perform pre-processing, tagging, compression,
et cetera.
And that's it for this exam cram.