All organizations, regardless of their industry or sector, need to control their most valuable asset: their data. Data is used to make strategic business decisions, helps to manage and mitigate risk, and ensures compliance with government and industry regulations.
While many organizations focus on securely storing their data, they also need to consider their data’s entire lifecycle: collection, ingestion, usage, storage, backup, archiving, loss prevention, and deletion. Data governance is a principled approach to managing that lifecycle.
Proper data governance helps to improve data reliability and quality, which provides a foundation for analytics, machine learning, and artificial intelligence. It also provides consistency and accuracy of data across clouds and platforms. But it all starts with finding the right database services for your specific use case—which is not always a straightforward task.
Choosing a Cloud database service
Google Cloud offers cloud database services for single, hybrid, and multi-cloud deployments. A single cloud deployment is the simplest, whether you’re creating a new cloud database on a Google Cloud database service or migrating an existing workload from an on-premise database or another cloud provider.
In some cases, your cloud applications may still require access to on-premise resources (and vice-versa). If so, you’ll need to consider reliable data transfer—for example, with a relational database management system such as MySQL—to avoid inconsistencies or reformatting.
These days, most organizations are using multiple clouds, which adds to the complexity of a migration. Data governance principles apply to each cloud instance and any integrations and transfers between them.
Fortunately, a Google Cloud database service allows you to combine the benefits of Google Cloud with other cloud providers’ database services. That means you’ll benefit from multiple fail-safes, but you’ll need to be sure systems are properly integrated and data is seamlessly available across clouds.
How to choose a Google Cloud database service
Multiple Google Cloud database services exist; the right option will depend on your specific use case. For example, do you require transactional processing or analytical processing? What tools and services will you need to integrate with?
Here are some of your options and key considerations:
Cloud SQL: This is a fully managed, relational Google Cloud database service. Compatible with SQL Server, MySQL, and PostgreSQL, it’s a good option for enterprise resource planning (ERP), customer relationship management (CRM), and ecommerce/web applications.
With a standard API across database engines, Cloud SQL is ideal for lifting and shifting on-premise SQL databases to the cloud and working with large-scale SQL data analytics. You can also integrate it with BigQuery, Kubernetes, and Compute Engine.
AlloyDB: As a fully managed PostgreSQL-compatible Google Cloud database service, AlloyDB combines Google’s expertise with the popular open-source PostgreSQL database engine. Together, they offer high performance, availability, and scale for the most demanding enterprise workloads. When it comes to analytical queries, AlloyDB is 100 times faster than standard PostgreSQL.
Cloud Spanner: This fully managed, relational Google Cloud database service offers high availability and unlimited scalability, so it’s ideal for applications such as order and inventory management, supply chain management, and financial trading.
What makes it different from Cloud SQL? Cloud Spanner allows you to combine relational structure with non-relational scalability, with added features like multi-language support.
Firestore: A fully managed, serverless NoSQL Google Cloud database service, Firestore easily aggregates data from multiple sources—including web, mobile, and IoT—and offers rapid application development with built-in cross-client sync. It can be integrated with Firebase, Google’s mobile development platform, so it’s an ideal option for web and mobile apps, real-time analytics, and collaborative applications.
Cloud Bigtable: Another fully managed NoSQL Google Cloud database service, Bigtable is designed for large-scale, low-latency workloads. With high throughput and consistent millisecond latencies, it’s ideal for financial analyses, IoT analytics, and personalized marketing applications. It can also be integrated with Google BigQuery and Apache tools like Hadoop.
Memorystore: This is a fully managed in-memory Google Cloud database service, which means it’s highly secure, scalable, and available—and best for in-memory and transient data stores. It’s compatible with Memcached and Redis protocols and provides sub-millisecond latency, making it ideal for real-time analytics and machine-learning applications.
Bare Metal Solution for Oracle: This option offers high performance and availability on pre-configured infrastructure for lifting and shifting Oracle workloads to Google Cloud. It’s a good fit for database modernization and migrating legacy Oracle workloads to the cloud.
Oracle Database@Google Cloud: A new partnership between Google and Oracle will allow you to combine Google Cloud technologies with Oracle Cloud Infrastructure (OCI) to accelerate application migrations and modernization. This could be a good option if you’re not ready to give up your Oracle database but you’re looking to modernize your IT environment—and gain the benefits of Google Cloud infrastructure, tooling, and AI services, including data and analytics, Vertex AI, and Gemini foundation models.
Taking the next step
Choosing the right Google Cloud database service can help you leverage your data, maximize your resources, and govern your data throughout its lifecycle. But choosing the right database service is challenging, especially if your data structure could change and evolve down the road.
Pythian’s data management services for Google Cloud can help you find the right option—and migrate, manage, and modernize your data—for the fastest time to value. Get in touch with a Pythian Google Cloud expert to see how our team can help.
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