Assessing and migrating systems and data to support the solution (e.g., Google Cloud Migration Center)

Inventory Discovery and TCO Assessment

The first phase of a cloud migration is the assessment phase, where architects identify existing resources and system requirements. Using the Google Cloud Migration Center, teams can automate the process of Inventory Discovery to find all active data pipelines, infrastructure components, and datasets. This automated approach ensures that no critical system is overlooked and provides a complete view of the current environment.

An effective inventory categorizes assets into a separate data storage layer and data computation layer. Separating these layers improves system flexibility and reduces costs by allowing computation resources to run only when they are actively processing data. While organizing these layers, architects must select appropriate targets like Cloud Storage, BigQuery, or Cloud SQL for storage, and Dataflow, Dataproc, or Google Kubernetes Engine for compute. Additionally, evaluating network latency is critical to understand how quickly data can move between these decoupled components.

Calculating the Total Cost of Ownership (TCO) is necessary to understand the financial impact of migrating to Google Cloud. A thorough TCO assessment compares current on-premises expenditures against estimated cloud expenses, including storage, compute, and personnel training costs. This assessment must also account for networking costs, such as the egress fees charged when moving data out of existing environments. Google Cloud Migration Center assists in this step by analyzing resource utilization metrics like CPU and memory to rightsize the cloud infrastructure, preventing over-provisioning and ensuring a cost-effective design.

Migration Strategy Selection and Dependency Mapping

Before initiating a migration, architects must map all application dependencies and business constraints to organize systematic migration waves. Gathering a comprehensive inventory of legacy systems helps identify which databases and pipelines rely on each other to function. Mapping these dependencies prevents critical connection failures and helps the team determine whether to use online or offline transfer methods. This detailed mapping ensures that the migration timeline is realistic and does not disrupt daily business operations.

Architects choose from several migration strategies depending on budget, time constraints, and technical requirements:

• Rehosting, also known as lift and shift, moves applications directly to the cloud with minimal configuration changes.
• Replatforming transitions applications to managed services, such as Cloud Data Fusion, to reduce operational overhead without changing the core application code.
• Refactoring involves completely rearchitecting applications to utilize cloud-native features and scale more efficiently.

A secure and low-risk migration plan relies on extensive testing and risk management. Teams should perform dry runs in non-production environments to test the transition path without risking live systems. Defining clear success criteria and preparing fallback plans are essential steps to protect business continuity if technical issues arise during the migration. These preparation steps ensure that the team can safely roll back changes to a known working state if needed.

Data Transition and Integrity Management

Designing a successful migration requires an architecture plan that separates data storage from data computation. For storing datasets, architects choose Cloud Storage for unstructured objects, BigQuery for analytics, or Cloud SQL and Spanner for relational databases. Separating these storage resources from processing engines ensures that compute resources do not have to run constantly, which minimizes ongoing operational costs.

To securely transition large-scale datasets while maintaining data integrity, architects deploy specialized migration services. The Storage Transfer Service is used to move objects from other cloud providers or on-premises locations into Google Cloud. For relational databases, the Database Migration Service (DMS) manages the secure replication of schemas and data. Both services provide built-in monitoring to track migration health and ensure that data is not lost or corrupted during transit.

When migrating data pipelines, workflow automation tools help manage the Extract, Transform, Load (ETL) lifecycle. Services like Cloud Data Fusion, Dataflow, and Cloud Composer automate data flows and ensure information reaches its destination intact. During the final cutover, architects use continuous replication to keep the old and new databases synchronized. Reducing the DNS Time-to-Live (TTL) value allows for rapid network redirection, and rigorous post-migration testing confirms data consistency before final sign-off.