A managed relational cloud database service
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Why Interlink?Many consulting companies know SQL. Some are up to speed on Azure and how to utilize Azure to benefit your SQL environment, and a few understand how to license these solutions appropriately - i.e. take into consideration the cost of licensing and help ensure that the customer stays compliant. Interlink knows all three and can architect the right solution for your needs.
Countless consulting companies can sell PaaS but knowing how to integrate that with your SQL platform is what Interlink does. We properly size the Database Throughput Units (DTUs), which are based on a blended measure of CPU, memory, reads, and writes. By properly sizing DTUs in PaaS, we can help ensure you don’t under or over provision your processor, memory, and disk resources. With Interlink, you can worry less about the management of your SQL platform and focus more on value-adds to your business. 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. This is part of our series of comprehensive guides on cloud storage technology. In this article, you will learn:
Deploying Databases on Google Cloud: Single Cloud, Hybrid, and Multicloud DeploymentGoogle Cloud Platform (GCP) supports three primary deployment models: single cloud, hybrid, and multicloud. Single Cloud DeploymentThe simplest deployment model is to deploy databases on Google Cloud only, via:
Hybrid Deployment: Google Cloud and On-Premises ResourcesHybrid 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:
The following diagram illustrates an example of a hybrid architecture with Google Cloud and on-premises systems. Image sourceMulticloud Deployment: Google Cloud and Other Cloud ProvidersMulticloud 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:
The following diagram illustrates a multicloud deployment involving GCP and another public cloud provider. Image sourceGCP offers several Google Cloud database services you can choose from. Below is an introduction to each. Cloud SQLCloud 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:
Cloud SpannerCloud 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:
BigQueryBigQuery 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:
Cloud BigtableCloud 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:
Cloud FirestoreCloud 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:
Firebase Realtime DatabaseRealtime 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:
Cloud MemorystoreCloud 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:
How to Choose a Google Cloud Database ServiceEven 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 Cloud Spanner 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/Datastore 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 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 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 ONTAPNetApp 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 DatabaseCloud Firestore: An In-Depth LookCloud Firestore enables you to store web and mobile applications data, in Google Cloud Platform (GCP). You can leverage Cloud Firestore for real time synchronization between client applications, by using listeners. This article explains what Cloud Firestore is, how it works, and notes the differences between Cloud Firestore and Realtime Database. Including best practices for Cloud Firestore implementations. Read more: Cloud Firestore: An In-Depth Look. 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: 8 Types of Google Cloud Analytics: How to Choose? Understanding Google Cloud High AvailabilityHigh 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. Google Cloud MySQL: The Complete GuideThere are several ways to run MySQL on Google Cloud. You can use Google Cloud SQL, which is a managed Google Cloud service. Alternatively, you can use a Google Cloud Marketplace image to install MySQL on a Compute Engine instance. It is also possible to manually install MySQL on Compute Engine. This article provides an in-depth look at these three deployments options. Read more: Google Cloud MySQL: The Complete Guide Google Cloud PostgreSQL: Managed or Self Managed?Google Cloud PostgreSQL is a fully managed Google Cloud database service, which allows you to automatically provision and manage PostgreSQL database instances. Learn about the Google Cloud PostgreSQL managed service, and the pros and cons of managed vs. self-managed PostgreSQL on Google Cloud. Read more: Google Cloud PostgreSQL: Managed or Self Managed? Google Cloud Big Data: Building Your Big Data Architecture on GCPThe Google Cloud Platform provides multiple services that support big data storage and analysis. Possibly the most important is BigQuery, a high performance SQL-compatible engine that can perform analysis on very large data volumes in seconds. Learn how Google Cloud Big Data services can help you build a robust big data infrastructure. Read more: Google Cloud Big Data: Building Your Big Data Architecture on GCP. Google Cloud NoSQL: Firestore, Datastore, and BigtableGoogle’s cloud platform (GCP) offers a wide variety of database services. Of these, its NoSQL database services are unique in their ability to rapidly process very large, dynamic datasets with no fixed schema. Learn about the big three Google Cloud NoSQL offerings, providing high performance data access for web applications, mobile applications, and huge scale datasets. Read more: Google Cloud NoSQL: Firestore, Datastore, and Bigtable. Google Cloud Data Lake: 4 Phases of the Data Lake LifecycleA data lake is a central repository designed to store, process, and protect large volumes of structured, semi-structured and unstructured data. You can store the data in its native format and use a variety of data without considering size limitations. Learn about the four phases in a Google Cloud data lake lifecycle, and the tools and services Google provides for implementing them. Read more: Google Cloud Data Lake: 4 Phases of the Data Lake Lifecycle. Google Cloud SQL: MySQL, Postgres and MS SQL on Google CloudGoogle 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. Read more Google Cloud SQL: MySQL, Postgres and MS SQL on Google Cloud. Google Cloud SQL Pricing, and Limits: A Cheatsheet for Cost OptimizationGoogle 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. Read more: Google Cloud SQL Pricing, and Limits: A Cheatsheet for Cost Optimization Should You Still Be Using Google Cloud Datastore?Google Cloud Datastore is a highly scalable, managed NoSQL database hosted on the Google Cloud Platform. Google has released Firestore, a new version of Datastore with several improvements and additional features. In future, existing Datastore databases will be automatically upgraded to Firestore. Read more: Should You Still Be Using Google Cloud Datastore? See Our Additional Guides on Key Cloud Storage TopicsWe have authored in-depth guides on several other topics that can also be useful as you explore the world of cloud storage. Cloud File SharingFile shares support some of the most important workloads that enterprise businesses rely on, and the resources of the public cloud have created interesting new possibilities. Every major public cloud provider now offers its own cloud file sharing service, each with its own target workloads and considerations. But not every enterprise will find what they’re looking for in a fully managed, all-cloud service. See top articles in our cloud file sharing guide:
Multicloud StorageMulticloud strategies are becoming more popular as organizations seek to optimize their cloud services and deployments. These strategies can help you prevent vendor lock-in, increase your flexibility, and help you optimize costs. This guide explains what multicloud storage is, how it works, what it’s used for, the core requirements for this storage, and how Cloud Volumes ONTAP supports it. See top articles in our multicloud storage guide:
AWS Database ServicesAWS offers a range of database services and support to try and meet all its clients needs. Many of these services are fully managed to help reduce your IT workload and enable you to store and use data as simply as possible. This guide explains what AWS database support is available, what database services are available, and how you can migrate your databases to AWS. See top articles in our AWS database services guide:
AWS Snapshots for Amazon EBSSnapshots are a common method for natively backing up cloud data and services. This method enables you to save point in time backups which can be restored when needed. This guide explains what types of storage snapshots are available, what AWS snapshots are, and how to use AWS snapshots. See top articles in our AWS snapshots guide:
Azure BackupAzure provides a wide variety of services to its users to help you manage your cloud data and services reliably. Azure Backup is one such service that can help provide data loss protection and peace of mind. This guide explains what Azure Backup is and how to use it to backup your Azure data. See top articles in our Azure Backup guide:
Azure File StorageStoring file data in Azure is simple through Azure File Storage service. This service enables you to store files across cloud and on-premises resources, enabling you to flexibly and securely share data and workflows. This guide explains what Azure File Storage is, common use cases for Files, management concepts and components of the service, how data is accessed and the architecture of the service, and some best practices for securing your data. See top articles in our Azure file storage guide:
Google Cloud StorageGoogle Cloud offers a variety of storage options for you to choose from. These services form the base of many other services in the cloud and understanding what your options are can help you manage your cloud more efficiently. This guide explains what Google Cloud Storage options exist and their common uses. See top articles in our Google Cloud storage guide:
Google Cloud Database ServicesGoogle Cloud’s specialty is flexibility and integration of services and this extends to its database services. In Google Cloud you have a wide variety of database deployments, models, and support to choose from. This guide explains your options for deploying databases in the cloud, what Google Cloud database services are available, and how to choose the right service for you. See top articles in our Google Cloud database guide:
Kubernetes StorageSoftware developers and DevOps engineers are packaging applications into lightweight units called containers. Kubernetes helps manage and scale containers across clusters of physical machines. In this environment, Kubernetes storage becomes a significant challenge. By default, containers are ephemeral, meaning that any transient data on the container is lost when it shuts down. However, Kubernetes provides several options for persistent storage. See top articles in our Kubernetes guide:
S3 StorageLearn the basics of storing data in Amazon Simple Storage Service (S3), Amazon’s first cloud service and still one of its most popular.
Which of the following is a managed relational cloud database service?Part of the Azure SQL family, Azure SQL Database is an always-up-to-date, fully managed relational database service built for the cloud.
What is relational database service in Azure?Azure SQL Database: It is a part of the Azure SQL Family. It is a scalable, relational database service built for the cloud and is based on Microsoft SQL Server Database Engine. It is available with three deployment options- Azure SQL Database server, Azure Virtual Network, and Azure SQL Database Serverless.
Which of the following is an Azure cloud based service that can be used to run complex queries across petabytes of data in a relational database?Azure SQL Data Warehouse is an elastic, globally available, cloud data warehouse that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.
What is a managed database in Azure?Azure SQL Managed Instance is the intelligent, scalable cloud database service that combines the broadest SQL Server database engine compatibility with all the benefits of a fully managed and evergreen platform as a service.
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