More about Google cloud database
- Understanding Google Cloud High Availability
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- Google Cloud BigQuery: How to Use Google Cloud BigQuery
- Oracle on Google Cloud: Two Deployment Options
- Google Cloud SQL Pricing and Limits: A Cheat Sheet
- SQL Server on Google Cloud: Two Deployment Options
- Google Cloud Database: The Right Service for Your Workloads
- Google Cloud SQL: MySQL, Postgres and MS SQL on Google Cloud
Google Cloud Platform (GCP) provides a wide range of computing resources, including database services. GCP offers three types of reference architectures for global data distribution—hybrid, multicloud, and regional distribution. When choosing a Google database service, you should take these architectures into consideration.
In this post, we’ll explain data distribution in GCP, and provide an overview of popular Google cloud database services, including key considerations when assessing and choosing a service. We’ll also show how NetApp Cloud Volumes ONTAP can help centralize and simplify the management of Google cloud database resources.
In this article, you will learn:
- Deploying Databases on Google Cloud: Single, Hybrid, and Multicloud
- Top 7 Google cloud database services
- How to choose a Google cloud database
- Google cloud database management with Cloud Volumes ONTAP
Deploying Databases on Google Cloud: Single Cloud, Hybrid, and Multicloud Deployment
Google Cloud Platform (GCP) supports three primary deployment models: single cloud, hybrid, and multicloud.
Single Cloud Deployment
The simplest deployment model is to deploy databases on Google Cloud only, via:
- Creating of new cloud databases on Google
- “Lift and shift” of existing workloads from on-premise to the cloud, and discontinuing the on-premise database resources
Hybrid Deployment: Google Cloud and On-Premises Resources
Hybrid deployments are useful when you have applications in the cloud that need to access on-premises databases or vice versa. For example, if you are performing marketing analytics on-premises and need to access customer databases hosted in the cloud.
There are three primary considerations for deployment a database in a hybrid model - with some data on Google Cloud and some on-premises:
- Master database—you need to decide whether your master database is stored on-premises or in the cloud. If you choose the cloud, GCP resources can act as a data hub for on-premises resources. If you choose on-premises, your in-house resources can sync data to the cloud for remote use or backup. This can enable you to maintain mirrored databases, providing a failover in case of disaster.
- Managed services—these services are only available for resources in the cloud. If you need to use a hybrid application with your data, you may not be able to access managed services for that application. For example, if you are creating a hybrid cloud database, you cannot fully benefit since your on-premises resources aren’t managed. These services include scalability, redundancy, and automated backups. You can, however, use third-party managed services.
- Portability—the type of data store you choose affects the portability of your data. To ensure that data can be transferred reliably, and that configuration and administration are consistent, you need to consider a cross-platform store, such as MySQL. Using homogeneous databases on-premises and in the cloud ensures that you do not have to reformat or rescheme data. This enables you to easily transfer it as needed.
The following diagram illustrates an example of a hybrid architecture with Google Cloud and on-premises systems.
Multicloud Deployment: Google Cloud and Other Cloud Providers
Multicloud deployments enable you to combine databases deployed on Google Cloud with database services from other cloud providers. This can help you create multiple fail-safes, more effectively distribute your database, or take advantage of a wider array of proprietary cloud features.
When considering a multicloud deployment you should be aware of the following:
- Integration—it is important to ensure that client systems can smoothly access databases, regardless of the cloud they are deployed on. You can use open-source client libraries to make databases seamlessly available across clouds, such as jclouds (see the JDBC guide).
- Database migration—with multiple cloud providers, you may need to migrate data between clouds. To migrate databases into GCP, you will need to use database replication tools or export/import processes. There are several Google Cloud migration tools you can use to migrate data into Google Cloud, such as the Google Storage Transfer service.
The following diagram illustrates a multicloud deployment involving GCP and another public cloud provider.
Google Cloud Database Services
GCP offers several Google Cloud database services you can choose from. Below is an introduction to each.
Cloud SQL is a fully managed, relational Google Cloud database service that is compatible with SQL Server, MySQL, and PostgreSQL. It includes features for automated backups, data replication, and disaster recovery to ensure high availability and resilience. You can integrate this service with Compute Engine, App Engine, BigQuery, and Kubernetes.
Common use cases for Cloud SQL include:
- Lift and shift of on-premise SQL databases to the cloud
- Large-scale SQL data analytics
- Supporting content management system (CMS) data storage and scalability
- Managing databases using Infrastructure as Code (IaC)
- Development and deployment of containerized applications and microservices
Cloud Spanner is another fully managed, relational Google Cloud database service. It differs from Cloud SQL by focusing on enabling you to combine the benefits of relational structure and non-relational scalability. It provides strong consistency across rows and high-performance operations. It includes features for automatic replication, built-in security, and multi-language support.
Use cases for Cloud Spanner include:
- Supply chain management and manufacturing
- Financial trading, analysis, and prediction
- Logistics and transportation
BigQuery is a fully managed, serverless data warehouse. You can use it to perform data analyses via SQL and query streaming data. This service includes a built-in Data Transfer Service to help you migrate data from on-premises resources, including Teradata.
BigQuery includes features for machine learning, business intelligence, and geospatial analysis. These features are provided through BigQuery ML, BI Engine, and GIS.
Use cases for BigQuery include:
- Process analytics and optimization
- Big data processing and analytics
- Machine learning-based behavioral analytics and predictions
- Data warehouse modernization
Cloud Bigtable is a fully managed NoSQL Google Cloud database service. It is designed for large operational and analytics workloads. Cloud Bigtable includes features for high availability, zero-downtime configuration changes, and sub-10ms latency. You can integrate it with a variety of tools, including Apache tools like Hadoop, TensorFlow, and Google Cloud services like BigQuery.
Use cases for Cloud Bigtable include:
- Financial analysis and prediction
- Internet of things (IoT) data ingestion, processing, and analytics
- Marketing applications, including hyper personalization
Cloud Firestore is a fully managed, serverless NoSQL Google Cloud database designed for the development of serverless apps. You can use it to store, sync, and query data for web, mobile, and IoT applications. It includes features for offline support, live synchronization, and built-in security. You can integrate Firestore with Firebase, GCP’s mobile development platform, for easier app creation and management.
Use cases for Cloud Firestore include:
- Mobile and web applications with both online and offline capabilities
- Multi-user, collaborative applications
- Real-time analytics
- Social media applications
- Gaming forums and leaderboards
Firebase Realtime Database
Realtime Database is a NoSQL Google Cloud database that is part of the Firebase platform. It enables you to store and sync data in real-time and includes caching capabilities for offline use. Realtime Database also enables you to implement declarative authentication, matching users by identity or pattern matching. It includes mobile and web software development kits (SDKs) for easier and faster app development.
Use cases for Firebase Realtime Database include:
- Development of apps that work across devices
- Ad optimization and personalization
- Third-party payment processing
- Integration of machine learning
Cloud Memorystore is a fully managed, in-memory Google Cloud data store. It is designed to be secure, highly available, and scalable. Cloud Memorystore enables you to create application caches with sub-millisecond latency for data access. It is compatible with Memcached and Redis protocols.
Use cases for Cloud Memorystore include:
- Lift and shift migration of applications
- Machine learning applications
- Real-time analytics
- Low latency data caching and retrieval
How to Choose a Google Cloud Database Service
Even after you explore your database options in Google Cloud, deciding which are the right options for you can be a challenge. When considering your options, keep in mind that many organizations need and can benefit from using multiple services. This enables you to optimize your implementations according to database capabilities, rather than trying to adapt a database service to fit all needs.
Cloud SQL is a good option when you need relational database capabilities but don’t need storage capacity over 10TB or more than 4000 concurrent connections. You also need to be skilled at on-premise management.
Cloud Spanner is a good option when you plan to use large amounts of data (more than 10TB) and need transactional consistency. It is also good if you want to use sharding for higher throughput and accessibility.
If you know or think that you might eventually need to be able to horizontally scale your Google Cloud database, Cloud Scanner is a better option than Cloud SQL. If you start with Cloud SQL and need to eventually move to Cloud Spanner, be prepared to re-write your application in addition to migrating your database.
Cloud Firestore or Datastore are good options when you plan to focus on app development and need live synchronization and offline support.
If you need to store unstructured data in JSON documents, Cloud Datastore is the recommended option. This is in comparison to if you need to store structured data, in which case Cloud Spanner is recommended.
An additional factor to consider is whether you need atomicity, consistency, isolation, durability (ACID) compliance. If so, you need to choose Cloud Spanner since Cloud Datastore only offers atomic and durable transactions.
Cloud Bigtable is a good option if you are using large amounts of single key data. In particular, it is good for low-latency, high throughput workloads.
If you need to perform single-region analytics, Cloud Bigtable is preferred over Cloud Spanner. However, if you need multi-regional operations, Cloud Spanner is the recommended solution. For example, Cloud Bigtable is a good option for a time series app created for DevOps monitoring. Meanwhile, Cloud Spanner is the recommended option for an infrastructure monitoring platform designed for software as a service (SaaS) offering.
Cloud Memorystore is a good option if you are using key-value datasets and your primary concern is transaction latency.
If you do not need disk-based data persistence and are only using the service for caching, Cloud Memorystore should be your choice. However, if you are concerned about issues like cache to database consistency or stream processing, you should choose Cloud Bigtable. Likewise, any time that your volume of data is too big to fit into memory, Cloud Memorystore is not the best option for you.
Google Cloud Database Management with Cloud Volumes ONTAP
NetApp Cloud Volumes ONTAP, the leading enterprise-grade storage management solution, delivers secure, proven storage management services on AWS, Azure and Google Cloud. Cloud Volumes ONTAP supports up to a capacity of 368TB, and supports various use cases such as file services, databases, DevOps or any other enterprise workload, with a strong set of features including high availability, data protection, storage efficiencies, Kubernetes integration, and more.
In particular, Cloud Volumes ONTAP helps in addressing database workloads challenges in the cloud, and filling the gap between your cloud-based database capabilities and the public cloud resources it runs on.
Learn More About Google Cloud Database Services
Google Cloud SQL: MySQL, Postgres, and MS SQL on Google Cloud
Google Cloud SQL is a managed database service that allows you to run Microsoft SQL Server, MySQL, and PostgreSQL on Google Cloud. The service provides replication, automated backups, and failover to ensure high-availability and resilience. In addition, it provides an easy and fast way to deploy and operate an SQL database in your cloud.
This post introduces the Google Cloud SQL service, explains the features that Google provides for each type of database, the costs, and how to start your first database.
SQL Server on Google Cloud: Two Deployment Options
Learn two key ways to deploy SQL Server on Google Cloud. You can leverage Google Cloud’s fully managed service, called Cloud SQL. Alternatively, you can go the self-managed way by running SQL Server on Compute Engine instances and using a managed storage service.
Google Cloud SQL Pricing, and Limits: A Cheat Sheet for Cost Optimization
Google Cloud SQL is a database service that offers managed versions of SQL Server, MySQL, and PostgreSQL. This service can provide significant benefits over on-premises implementations. However, before signing up, you should consider both pricing and its limitations.
This article explains the various pricing breakdowns of SQL database services in Google Cloud, covers the limitations of Google Cloud SQL, and highlights how you can optimize costs with Cloud Volumes ONTAP.
Oracle on Google Cloud: Two Deployment Options
While Google Cloud does not offer a managed service for Oracle, it is still possible to deploy Oracle databases on Google Cloud. You can do that using either partner-managed Oracle instances or custom Oracle instances. This article explains the main differences between the two options.
Read more in Oracle on Google Cloud: Two Deployment Options
Google Cloud BigQuery: How and When to Use Google Cloud BigQuery to Store Your Data
Databases store massive amounts of data, and to analyze it, Google has provided users with an easy Big Data tool in BigQuery. This Google service allows data to be leveraged for business insights, however, there are some limitations to using the service.
In this post we’ll take a closer look at Google Cloud BigQuery, look under the hood to see how it works, and show you step-by-step how to set it up to start analyzing your data. We’ll also see how it can be used in conjunction with Cloud Volumes ONTAP to overcome some of its major challenges.
8 Types of Google Cloud Analytics: How to Choose?
Google Cloud Analytics services provide various capabilities you can use to leverage data to improve customer experience and democratize the use of data across various collaborators. Learn how to build efficient architectures while using Google services.
Read more in 8 Types of Google Cloud Analytics: How to Choose?
Understanding Google Cloud High Availability
High availability provides a consistent level of uptime, ensuring workloads experience minimal failure. In GCP, this is achieved by leveraging 24 regions and 73 availability zones and a Compute Engine.
Read more: Understanding Google Cloud High Availability
VMware on Google Cloud: A Deployment Roadmap
Google Cloud VMware Engine provides management for simplifying VMware deployments on Google Cloud. Using VMware Engine, you can easily migrate your resources. This article discusses key techniques and considerations for successful deployments.
Read more: VMware on Google Cloud: A Deployment Roadmap