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Last reviewed 2025-01-23 UTC
With the handover pattern, the architecture is based on using
Google Cloud-provided storage services to connect a private computing
environment to projects in Google Cloud. This pattern applies primarily to
setups that follow the
analytics hybrid multicloud architecture pattern,
where:
Workloads that are running in a private computing environment or in
another cloud upload data to shared storage locations. Depending on use
cases, uploads might happen in bulk or in smaller increments.
Google Cloud-hosted workloads or other Google services (data
analytics and artificial intelligence services, for example) consume data
from the shared storage locations and process it in a streaming or batch
fashion.
Architecture
The following diagram shows a reference architecture for the handover
pattern.
The preceding architecture diagram shows the following workflows:
On the Google Cloud side, you deploy workloads into an
application VPC. These workloads can include data processing, analytics,
and analytics-related frontend applications.
To securely expose frontend applications to users, you can use
Cloud Load Balancing or API Gateway.
A set of Cloud Storage buckets or Pub/Sub queues uploads data
from the private computing environment and makes it available for further
processing by workloads deployed in Google Cloud. Using
Identity and Access Management (IAM) policies,
you can restrict access to trusted workloads.
Use
VPC Service Controls
to restrict access to services and to minimize unwarranted data
exfiltration risks from Google Cloud services.
In this architecture, communication with Cloud Storage buckets,
or Pub/Sub, is conducted over public networks, or through
private connectivity using VPN, Cloud Interconnect, or
Cross-Cloud Interconnect. Typically, the decision on how to connect
depends on several aspects, such as the following:
Expected traffic volume
Whether it's a temporary or permanent setup
Security and compliance requirements
Variation
The design options outlined in the
gated ingress pattern,
which uses Private Service Connect endpoints for Google APIs, can also
be applied to this pattern.
Specifically, it provides access to Cloud Storage, BigQuery,
and other Google Service APIs. This approach requires private IP addressing over
a hybrid and multicloud network connection such as VPN, Cloud Interconnect
and Cross-Cloud Interconnect.
Best practices
Lock down access to Cloud Storage buckets and
Pub/Sub topics.
When applicable, use cloud-first, integrated data movement solutions
like the Google Cloud
suite of solutions.
To meet your use case needs, these solutions are designed to efficiently
move, integrate, and transform data.
Assess the different factors that influence the data transfer options,
such as cost, expected transfer time, and security. For more
information, see
Evaluating your transfer options.
To minimize latency and prevent high-volume data transfer and movement over
the public internet, consider using Cloud Interconnect or
Cross-Cloud Interconnect, including accessing
Private Service Connect endpoints within your Virtual Private Cloud for
Google APIs.
To protect Google Cloud services in your projects and to mitigate
the risk of data exfiltration, use VPC Service Controls. These service
controls can specify service perimeters at the project or VPC network level.
Communicate with publicly published data analytics workloads that are
hosted on VM instances through an API gateway, a load balancer, or a
virtual network appliance. Use one of these communication methods for added
security and to avoid making these instances directly reachable from the
internet.
If internet access is required,
Cloud NAT
can be used in the same VPC to handle outbound traffic from the instances
to the public internet.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-01-23 UTC."],[[["\u003cp\u003eThe handover pattern uses Google Cloud storage services to bridge data between private computing environments and Google Cloud projects, especially within analytics hybrid multicloud architectures.\u003c/p\u003e\n"],["\u003cp\u003eData is uploaded from private environments to shared Cloud Storage buckets or Pub/Sub queues, where Google Cloud workloads can then consume and process it.\u003c/p\u003e\n"],["\u003cp\u003eAccess to Cloud Storage and Pub/Sub can be secured using IAM policies and VPC Service Controls, limiting access to trusted workloads and minimizing data exfiltration risks.\u003c/p\u003e\n"],["\u003cp\u003eConnectivity between private environments and Google Cloud can be over public networks, VPN, Cloud Interconnect, or Cross-Cloud Interconnect, depending on factors like traffic volume, security, and setup duration.\u003c/p\u003e\n"],["\u003cp\u003eTo minimize latency and data movement over public networks, utilize Cloud Interconnect or Cross-Cloud Interconnect, and for added protection, use Private Service Connect endpoints within your Virtual Private Cloud for accessing Google APIs.\u003c/p\u003e\n"]]],[],null,["# Handover patterns\n\nWith the *handover* pattern, the architecture is based on using\nGoogle Cloud-provided storage services to connect a private computing\nenvironment to projects in Google Cloud. This pattern applies primarily to\nsetups that follow the\n[*analytics hybrid multicloud* architecture pattern](/architecture/hybrid-multicloud-patterns#analytics-hybrid-multicloud-patterns),\nwhere:\n\n- Workloads that are running in a private computing environment or in another cloud upload data to shared storage locations. Depending on use cases, uploads might happen in bulk or in smaller increments.\n- Google Cloud-hosted workloads or other Google services (data analytics and artificial intelligence services, for example) consume data from the shared storage locations and process it in a streaming or batch fashion.\n\nArchitecture\n------------\n\nThe following diagram shows a reference architecture for the handover\npattern.\n\nThe preceding architecture diagram shows the following workflows:\n\n- On the Google Cloud side, you deploy workloads into an application VPC. These workloads can include data processing, analytics, and analytics-related frontend applications.\n- To securely expose frontend applications to users, you can use Cloud Load Balancing or API Gateway.\n- A set of Cloud Storage buckets or Pub/Sub queues uploads data from the private computing environment and makes it available for further processing by workloads deployed in Google Cloud. Using Identity and Access Management (IAM) policies, you can restrict access to trusted workloads.\n- Use [VPC Service Controls](/vpc-service-controls) to restrict access to services and to minimize unwarranted data exfiltration risks from Google Cloud services.\n- In this architecture, communication with Cloud Storage buckets, or Pub/Sub, is conducted over public networks, or through private connectivity using VPN, Cloud Interconnect, or Cross-Cloud Interconnect. Typically, the decision on how to connect depends on several aspects, such as the following:\n - Expected traffic volume\n - Whether it's a temporary or permanent setup\n - Security and compliance requirements\n\nVariation\n---------\n\nThe design options outlined in the\n[*gated ingress* pattern](/architecture/hybrid-multicloud-secure-networking-patterns/gated-ingress),\nwhich uses Private Service Connect endpoints for Google APIs, can also\nbe applied to this pattern.\nSpecifically, it provides access to Cloud Storage, BigQuery,\nand other Google Service APIs. This approach requires private IP addressing over\na hybrid and multicloud network connection such as VPN, Cloud Interconnect\nand Cross-Cloud Interconnect.\n\nBest practices\n--------------\n\n- Lock down access to Cloud Storage buckets and Pub/Sub topics.\n- When applicable, use cloud-first, integrated data movement solutions like the Google Cloud [suite of solutions](/data-movement). To meet your use case needs, these solutions are designed to efficiently move, integrate, and transform data.\n- Assess the different factors that influence the data transfer options,\n such as cost, expected transfer time, and security. For more\n information, see\n [Evaluating your transfer options](/architecture/migration-to-google-cloud-transferring-your-large-datasets#step_3_evaluating_your_transfer_options).\n\n- To minimize latency and prevent high-volume data transfer and movement over\n the public internet, consider using Cloud Interconnect or\n Cross-Cloud Interconnect, including accessing\n Private Service Connect endpoints within your Virtual Private Cloud for\n Google APIs.\n\n- To protect Google Cloud services in your projects and to mitigate\n the risk of data exfiltration, use VPC Service Controls. These service\n controls can specify service perimeters at the project or VPC network level.\n\n - You can [extend service perimeters](/vpc-service-controls/docs/overview#hybrid_access) to a hybrid environment over an authorized VPN or Cloud Interconnect. For more information about the benefits of service perimeters, see [Overview of VPC Service Controls](/vpc-service-controls/docs/overview).\n- Communicate with publicly published data analytics workloads that are\n hosted on VM instances through an API gateway, a load balancer, or a\n virtual network appliance. Use one of these communication methods for added\n security and to avoid making these instances directly reachable from the\n internet.\n\n- If internet access is required,\n [Cloud NAT](/nat/docs)\n can be used in the same VPC to handle outbound traffic from the instances\n to the public internet.\n\n- Review the\n [general best practices](/architecture/hybrid-multicloud-secure-networking-patterns/general-best-practices)\n for hybrid and multicloud networking topologies."]]