All organizations, regardless of their industry or sector, must ensure they 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 most people focus on securely storing data, organizations 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 is the 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—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 Google Cloud or migrating an existing workload from an on-premise database or another cloud provider. In some cases, however, your cloud applications still need access to on-premise resources (and vice-versa). You’ll need to consider reliable data transfer—for example, with a relational database management system such as MySQL—to avoid inconsistencies or reformatting. To add to the complexity, most organizations are using multiple clouds. Data governance principles apply to each cloud instance and any integrations and transfers between them. Fortunately, Google Cloud allows you to combine its platform's database services with other cloud providers' database services. You’ll benefit from multiple fail-safes, but 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 for choosing:
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 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. Indeed, when it comes to analytical queries, it’s 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? It 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 data store, which means it’s highly secure, scalable, and available—and best for in-memory and transient data stores. It is 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 ideal for database modernization and migrating legacy Oracle workloads to the cloud.
Taking the next step
Choosing the right 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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