aws.amazon.com Open in urlscan Pro
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AWS Resource Access ManagerSimple, secure service to share AWS resources
AWS Secrets ManagerRotate, manage, and retrieve secrets
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AWS STORAGE BLOG


MIGRATING GOOGLE CLOUD STORAGE TO AMAZON S3 USING AWS DATASYNC

by Brian Choi | on 25 MAY 2022 | in Amazon EC2, Amazon Simple Storage Service
(S3), AWS DataSync, Intermediate (200), Migration & Transfer Services, Storage |
Permalink | Comments |  Share
 * 
 * 
 * 
 * 
 * 

Organizations sometimes need to migrate large volumes of object data from one
cloud provider to another. Reasons for this type of migration may include data
consolidation, workload migration, disaster recovery, or the end of a discount
program. Migrations typically require end-to-end encryption, the ability to
detect changes, object validation, network throttling, monitoring, and cost
optimization. Building such solutions are time-consuming, expensive, and
difficult to scale. Furthermore, migrating between public cloud providers limits
certain options such as physically accessing the storage devices at the data
center.

In this blog, I walk through migrating object data from Google Cloud Storage to
Amazon Simple Storage Service (S3) using AWS DataSync. I start with an overview
of AWS DataSync and highlight specific features for this use case. Then, I walk
through configuring DataSync to Amazon S3.


AWS DATASYNC OVERVIEW

AWS DataSync is a data movement service that simplifies, automates, and
accelerates moving data to and from AWS Storage services, between AWS Storage
and Google Cloud Storage, and between AWS Storage and Microsoft Azure Files. AWS
DataSync features include data encryption, validation, monitoring, task
scheduling, auditing, and scaling.

Specifically for Google Cloud Storage, AWS DataSync has built-in integration
using the Google Storage XML API. This is a RESTful interface that lets
applications manage Google Cloud Storage objects in a programmatic way. DataSync
connects to the Google Storage XML API with a Hash-based Message Authentication
Code (HMAC) key. The Google Cloud Platform’s (GCP)  HMAC key contains access ID
and a secret that can be configured with appropriate roles to access Google
Cloud Storage bucket objects. With AWS DataSync, you don’t need to program any
custom code to migrate data from Google Cloud Storage, and you get the benefit
of all of the DataSync features.


AWS DATASYNC COMPONENTS AND TERMINOLOGY

DataSync has four components for data movement: task, locations, agent, and task
execution. Figure 1 shows the relationship between the components and the
attributes that I’ll use for the tutorial.



Figure 1: AWS DataSync four primary components

 * Agent: A virtual machine (VM) that reads data from, or writes data to, a
   self-managed location. In this tutorial, the DataSync agent is mapped to an
   Amazon EC2 instance running inside an Amazon VPC.
 * Location: The source and destination location for the data migration. In this
   tutorial, the source location is object storage pointing to the Google
   Storage XML API (https://storage.googleapis.com), with a specified Google
   Storage bucket name. The destination location is an Amazon S3 bucket.
 * Task: A task consists of one source and one destination location with a
   configuration that defines how data is moved. A task always moves data from
   the source to the destination. Configuration can include options such as
   include/exclude patterns, task schedule, bandwidth limits, and more.
 * Task execution: This is an individual run of a task, which includes
   information such as start time, end time, bytes written, and status.


HOW TO MIGRATE DATA FROM GOOGLE CLOUD STORAGE TO AMAZON S3 WITH AWS DATASYNC

For this tutorial, I walk through the steps to configure AWS DataSync to migrate
objects from Google Cloud Storage to Amazon S3. In Figure 2, I’ve laid out the
basic architecture for DataSync using a single DataSync agent. The source
objects to migrate reside in the Google Cloud Storage bucket. The destination is
the Amazon S3 bucket.



Figure 2: Single AWS DataSync agent architecture

DataSync is made up of several components, starting with a DataSync agent. In
this architecture, the agent is deployed as an Amazon Elastic Compute Cloud
(Amazon EC2) instance into a subnet within Amazon Virtual Private Cloud (Amazon
VPC). The subnet contains a DataSync VPC endpoint that allows the traffic to
flow privately from the DataSync agent to Amazon S3. The DataSync agent connects
to GCP Cloud Storage via the public internet using an HMAC key.


TUTORIAL PREREQUISITES

For this tutorial, you should have the following prerequisites:

 * An AWS account
 * AWS Command Line Interface (CLI)
 * Google Cloud Platform Cloud Storage bucket with source objects to transfer

SOURCE OVERVIEW – GOOGLE CLOUD STORAGE BUCKET

For our tutorial, I have a source Google Cloud Storage bucket named
demo-customer-bucket. You can create a storage bucket and upload objects to your
bucket to follow along. This bucket has two folders: business-data and
system-data. In this use case, I only want to transfer the objects in the
business-data folder and ignore the system-data folder. This is to demonstrate
DataSync’s capability to include or exclude folders from the source bucket.



Figure 3: Google Cloud Storage bucket as source

Inside the business-data folder, there are six files. My goal is to transfer the
five text files while ignoring the temporary file named log.tmp. This is to
demonstrate DataSync’s capability to exclude certain objects based on name
pattern.



Figure 4: Google Cloud Storage bucket objects as source


WALKTHROUGH

Now I am ready to build the solution to move data from Google Cloud Storage to
Amazon S3 using AWS DataSync. The following are the high-level steps:

 1. Create a Google Cloud Platform HMAC key.
 2. Create an Amazon S3 bucket as the destination.
 3. Create an IAM role to access the Amazon S3 bucket.
 4. Set up a network for the Amazon VPC.
 5. Deploy an Amazon EC2 DataSync agent.
 6. Create DataSync locations
    * Google Cloud Storage location
    * Amazon S3
 7. Create and run DataSync task
    * Create a DataSync task – Location
    * Create a DataSync task – Configure Settings
    * Run the DataSync task
 8. Verify the data migrated


STEP 1: CREATE A GCP HMAC KEY

DataSync agent uses an HMAC credential to authenticate to Google Cloud Platform
and manage objects in the Cloud Storage bucket. This requires creating a key for
a service account. You can follow the directions at Manage HMAC keys for service
accounts. When complete, you should have an access ID and a secret. Keep this
information in a secure location.

The service account principal needs sufficient permission for the DataSync agent
to connect and migrate the objects. You can assign a predefined role named
Storage Object Viewer to the service account principal as a way to grant this
permission.



Figure 5: Google Cloud Storage role permission

You can further limit the access of this role by adding a condition to only
allow when the resource name starts with projects/_/buckets/demo-customer-bucket
where the demo-customer-bucket is the name of the source bucket. Notice that the
condition uses a starts with condition. This allows the permission to be granted
for the bucket and the objects within the bucket using a single statement.

Common Expression Language (CEL) expression:

resource.name.startsWith(“projects/_/buckets/demo-customer-bucket”)



Figure 6: Google Cloud Storage role permission condition


STEP 2: CREATE AN AMAZON S3 BUCKET AS THE DESTINATION

Create a new Amazon S3 bucket that will be used as the destination for DataSync
transfer. Once you create the destination bucket, obtain the bucket Amazon
Resource Name (ARN) from the bucket’s Properties tab.



Figure 7: Amazon S3 bucket’s Amazon Resource Name


STEP 3: CREATE AN IAM ROLE TO ACCESS THE AMAZON S3 BUCKET

AWS DataSync needs to access the Amazon S3 bucket in order to transfer the data
to the destination bucket. This requires DataSync to assume an IAM role with
appropriate permission and trust relationship. Create a new role and attach a
policy that allows DataSync to read and write to your Amazon S3 bucket.


STEP 4: SET UP A NETWORK FOR THE AMAZON VPC ENDPOINT

Create the VPC, subnet, route table, and security group based on the network
requirements when using VPC endpoints.  Then, create a DataSync interface
endpoint. With this endpoint, the connection between an agent and the DataSync
service doesn’t cross the public internet and doesn’t require public IP
addresses.


STEP 5: DEPLOY AN AMAZON EC2 DATASYNC AGENT

The next step is to deploy an agent as an Amazon EC2 instance. The Amazon EC2
instance is launched using the latest DataSync Amazon Machine Image (AMI) into
the subnet from the previous step with the security group for agents. Once the
Amazon EC2 instance is running, create a DataSync agent component using the VPC
endpoint. Finally, activate your agent to associate it with your AWS account.


STEP 6: CREATE DATASYNC LOCATIONS

Create source and destination DataSync locations using the following steps.

GOOGLE CLOUD STORAGE LOCATION

 1. Open the AWS DataSync console and choose Locations. Then select the Create
    Location.
 2. For Location Type, select Object Storage.
 3. For Agents, select the agent that was activated in the previous step.
 4. For Server, type “storage.googleapis.com”
 5. For Bucket name, type the name of the Google Cloud Storage source bucket
    name. In this tutorial, this is “demo-customer-bucket.” Note that the bucket
    name is case-sensitive.
 6. For authentication, enter the Google HMAC key’s access ID and secret that
    were obtained in Step 1: Create a GCP HMAC Key.
 7. Select Create location.



Figure 8: DataSync source location for Google Cloud Storage

AMAZON S3 LOCATION

 1. Open the AWS DataSync console and choose Locations. Then select the Create
    Location.
 2. For Location Type, select Amazon S3.
 3. For the Amazon S3 bucket, select the destination Amazon S3 bucket from Step
    2: Create an Amazon S3 bucket as the destination.
 4. For IAM role, select the IAM role from Step 3: Create IAM Role to access
    Amazon S3.
 5. Select Create location.



Figure 9: DataSync destination location for Amazon S3


STEP 7: CREATE AND RUN AN AWS DATASYNC TASK

The next step is to create an AWS DataSync task. Follow these steps to create a
task starting with configuring the source and destination locations.

CREATE AN AWS DATASYNC TASK: LOCATION

 1. Open the AWS DataSync console and choose Tasks. Then select Create Task.
 2. For source location options, select Choose an existing location. For
    Existing locations, select the
    object-storage://storage.googleapis.com/<bucket name>/ from Step 6: Create
    DataSync locations. Then select Next.
 3. For Destination location options, select the Choose an existing location.
    Select s3://<bucket name>/ from Step 6: Create DataSync locations. Then
    select Next. This opens the Configure settings page.
 4. Select Next.

CREATE AN AWS DATASYNC TASK: CONFIGURE SETTINGS

The next step is to configure the settings for the DataSync task.

 1. For Task Name, enter the name of the task.
 2. For Data transfer configuration, select the Specific files and folders
    option in order to specify the subfolder using the include patterns.
 3. For Transfer mode, select Transfer only data that has changed.
    * Uncheck Keep deleted files. Notice that with this option unchecked, if the
      source object is deleted in the Google Cloud Storage bucket, the task will
      delete the corresponding object in the Amazon S3 bucket. This is behavior
      we want in the demo to keep Amazon S3 in sync with the source because the
      source bucket is assumed to be a live dataset.
    * Check Overwrite files. This means that if the task detects that the source
      and the target object are different, then the task will overwrite the
      target object in Amazon S3. This is the behavior we want to keep Amazon S3
      in sync with the source bucket.
    * For Include patterns, enter “/business-data/*”. Using the include
      patterns, you can filter the objects transferred to the scope of the
      location path. Note that the pattern value is case-sensitive.
    * For Exclude patterns, select Add pattern. For Pattern, enter “*.tmp”. By
      using this exclude filter, the task will ignore any objects that end with
      a .tmp name. Note that the pattern value is case-sensitive.
    * Expand the Additional settings and uncheck the Copy object tags. This
      prevents attempting to read tags on GCS which is not supported.
 4. For task logging, we want DataSync to write the detail logs to CloudWatch
    Logs. For Log level, select Log all transferred objects and files. For our
    demonstration, we have a small number of files. If you have a large volume,
    consider the cost for CloudWatch and select the logging level that meets
    your requirements. For more information, see Monitoring your task.
    * For CloudWatch log group, select Autogenerate. This will create the
      appropriate policy and CloudWatch log group. Then select Next. Review the
      task configuration.
 5. Select Create task, and wait for Task status to be Available.



Figure 10: DataSync task configuration settings

RUN THE AWS DATASYNC TASK

The final step is to start the DataSync task to transfer the files. The status
of the task will update as the task passes each phase. See Understanding task
execution status for possible statuses (phases) and meaning. You can also
monitor DataSync using Amazon CloudWatch.


STEP 8: VERIFY THE DATA MIGRATED

You can now verify the objects migrated from Google Cloud Storage to the Amazon
S3 bucket. Navigate to the Amazon S3 target bucket and open the business-data
folder. The five files should now be transferred to the Amazon S3 bucket. Notice
that the log.tmp file was not transferred because our task had an exclude filter
*.tmp, which excluded this log file from transferring.

You can run the task multiple times. Each time a task runs, AWS DataSync will
detect the changes between source and destination and only transfer the objects
that are new or modified. This allows you to transfer data that may be changing
on the source location.



Figure 11: Objects transferred from source to destination

DataSync also transfers the object’s custom metadata. In this tutorial, the
file-1.txt file had a custom metadata of Department with value of Math. This
information was transferred to the Amazon S3 metadata.



Figure 12: Object metadata transferred from source to destination


CLEANING UP

To avoid incurring future charges, delete the resources used in this tutorial.

 1. Inactivate the GCP HMAC key. Then Delete GCP HMAC key and Google Cloud
    Storage bucket.
 2. Delete DataSync task, locations then agent.
 3. Shut down the EC2 instance.
 4. Delete VPC endpoint.
 5. Delete Amazon S3 bucket.


CONCLUSION

In this blog post, I discussed using AWS DataSync to migrate object data from
Google Cloud Storage to Amazon S3, and walked through configuring AWS DataSync
to migrate objects from a Google Cloud Storage bucket to an Amazon S3 bucket. I
also demonstrated using AWS features to include and exclude source bucket
folders and objects.

Using AWS DataSync, you can simplify migrating a large volume of data from
Google Cloud Platform to Amazon S3. AWS DataSync has built-in capabilities that
allow you to connect to Google Cloud Storage through Google Storage XML API with
HMAC key. With the solution covered in this post, you can take advantage of
DataSync’s features, including the ability to run the tasks multiple times to
capture changes in the source dataset.

Here are additional resources to help you get started with AWS DataSync:

 * What’s New with AWS DataSync
 * AWS DataSync User Guide
 * AWS re:Post
 * AWS DataSync Primer – free one-hour, self-paced online course

Thank you for reading this post on migrating Google Cloud Storage to Amazon S3
using AWS DataSync. I encourage you to try this solution today. If you have any
comments or questions, please leave them in the comments section.

TAGS: Amazon Elastic Compute Cloud (Amazon EC2), Amazon Simple Storage Service
(Amazon S3), AWS Cloud Storage, AWS DataSync


BRIAN CHOI

Brian is a Senior Solution Architect at Amazon Web Service (AWS). Brian is part
of the Worldwide Public Sector group and helps Education customers in the US. He
specializes in the migration and modernization of workloads in AWS.

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