Database Migration and Modernization with Google Cloud Platform (GCP)

Database modernization is a must for enterprises that want to remain competitive in today’s fast-changing market. Legacy systems, especially custom applications, often require significant effort, cost, and specialized skills to upgrade. In some cases, modernizing the database also requires changes to the underlying application, which adds further complexity for the enterprise.

Modernizing databases on Google Cloud delivers better performance, robust scalability, advanced security, and reduced maintenance overhead. These capabilities make GCP a strong fit for organizations that must process large datasets, manage diverse data types, and meet evolving analytics and AI demands.

Need for Modernizing Database

Legacy databases may continue to function, but they often impose hidden costs and limitations that restrict business agility. High licensing and ongoing support fees strain budgets, while limited integration with modern analytics and AI tools hinders data-driven innovation. Scaling quickly to meet changing business demands becomes challenging, and outdated security measures increase the risk of compliance breaches.

By modernizing, organizations can:

• Reduce Total Cost of Ownership (TCO) through pay-as-you-go models and fewer on-prem resources
• Improve data accessibility and performance with on-demand scaling
• Strengthen security and compliance via built-in encryption and identity controls
• Unlock analytics and AI by making data available to cloud-native tools

Strategic Considerations for Modernization:

Modernize Your Databases on Google Cloud

Migrating legacy databases to Google Cloud removes hardware constraints, simplifies patch and capacity management, and reduces operational overhead. With GCP’s managed services, organizations gain high availability, pay-as-you-go economics, and the ability to focus engineering efforts on innovation rather than routine maintenance.

Key Google Cloud Services for Database Modernization

Google Cloud offers a comprehensive suite of services tailored to different workloads. This is whether you’re migrating transactional systems, analytics platforms, or real-time event stores.

Cloud SQL

• Fully Managed Relational Database
• Automatic Failover
• AI-Powered Assistance (Gemini)
• Enterprise Plus edition offering a 99.99% availability SLA

Supported Database Engines: SQL Server, PostgreSQL, MySQL

AlloyDB

• Operate with familiar PostgreSQL ecosystem
• High Performance & Hybrid Transactional and Analytical Processing (HTAP)
• Columnar engine for frequently used queries
• 99.99% uptime SLA

Supported Database Engine: PostgreSQL

Cloud Spanner

• Globally distributed, strongly consistent database
• Exabyte-scale database system with horizontal scalability
• ANSI-compliant multi-SQL dialects support
• 99.999% availability SLA

Supported Dialects: GoogleSQL, PostgreSQL

Bare Metal Solution

• Physical Machine
• Certified infrastructure for Oracle workloads and SAP HANA
• Massive Data Analytics
• 99.99% availability SLA

Recommended Database Technologies: Oracle, SAP HANA

Firestore

• Scalable NoSQL document store database
• Standard and Enterprise Edition with MongoDB compatibility
• Powerful Query Engine
• 99.999% availability SLA

Database Mode: Native Mode, Datastore Mode

Bigtable

• Ideal for structured, semi-structured, or unstructured data
• Stores terabytes or petabytes of data
• Cluster resizing without downtime
• 99.999% availability SLA

Spanner Graph

• Extension of Cloud Spanner, high-performance graph database
• Combines GQL and SQL together
• AI-powered insights
• 99.999% availability SLA for Multi and Dual-Region

Database Migration Service

• Streamlined, serverless migrations to Cloud SQL and beyond
• Native replication capabilities with minimal downtime
• Supports

Homogeneous – Lift and Shift

• MySQL to Cloud SQL for MySQL
• PostgreSQL to Cloud SQL, AlloyDB for PostgreSQL
• SQL Server to Cloud SQL for SQL Server

Heterogeneous – New type of Database

• Oracle to Cloud SQL, AlloyDB for PostgreSQL

TechM’s Database Modernization Strategy

Leveraging its deep partnership with Google Cloud, Tech Mahindra delivers end-to-end solutions for migrating structured, unstructured, and semi-structured databases to GCP. Our approach ensures a seamless transition from on-premises, legacy, or other cloud infrastructures, using the right mix of migration strategies to match business goals.

Migration Tools for Database Modernization

Google offers a variety of tools to help businesses modernize their databases, migrating them to the Google Cloud Platform and adopting cloud-native architectures. These tools are designed to speed up the migration process when working with large datasets or complex schemas, minimize manual errors, and offer dashboards and logs for real-time monitoring of migration status.

TechM Way of Modernizing the Database

Tech Mahindra approaches modernization with a business-first lens. We start by understanding the application’s function and the business drivers, then map operational and analytical workloads to the right target technologies. Our process evaluates legacy constraints, prioritizes migration candidates, and recommends hybrid or multi-cloud options where appropriate. The objective is to move from capital-intensive, on-prem setups to flexible, as-a-service models while minimizing disruption.

TechM’s Database Modernization Strategy

1. Assessment and Planning:

• Assess the current database landscape to determine migration feasibility and complexity, using tools like Google’s DMA, Ora2pg, Ispirer, and Striim.
• Evaluate the target database for data structure complexity, growth patterns, availability, security, and scalability needs.
• Create a tailored modernization roadmap aligned with technical requirements and business objectives.

2. Schema and Data Migration:

• Use Google’s Database Migration Service (DMS) for secure, serverless migration
• Enable Gemini-assisted conversion through DMS, TechM’s Sprinter (GCP Migration Workbench), Spanner Migration Tool, or alternative third-party solutions such as Ispirer and migVisor for schema and code conversion
• Create a migration job and configure source/destination connectivity
• Enable continuous replication (CDC) to minimize downtime – DMS, Striim, etc.

3. Validation and Testing:

• Use Google's Data Validation Tool (DVT), an open-source Python command-line utility, to verify data by comparing heterogeneous data sources and ensuring accuracy and quality throughout the migration process
• Perform data integrity checks: row counts, checksums, schema validation
• Test application connectivity and performance with the new database
• Configure the application to point to the migrated cloud database

4. Cutover and Optimization:

• Finalize replication and switch production traffic/workload to the cloud database
• Track performance and optimize queries, indexes, and configurations
• Build a reliable framework to manage security and compliance

5. Ongoing Management:

• Monitoring performance with cloud monitoring and logging
• Automate operations with cloud functions or Cloud Run

Conclusion

Modernizing your database on Google Cloud might seem like just a tech decision, but in reality, it’s a competitive advantage. It future-proofs your data strategy, improves reliability, and brings out the best value of your data, empowering advanced analytics, AI/ML use cases, and real-time insights that drive business growth.

By making use of GCP’s fully managed database services – such as Cloud SQL, Cloud Spanner, Firestore, and BigQuery-enterprises can reduce operational complexity, cost, and enhance system performance. Start by assessing your needs, selecting the correct database, and using GCP’s migration tools for a seamless transition. With the right strategy, your organization can change its data structure and drive innovation using Google Cloud.