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Choose The Right Database Service in GCP.

When it comes to choosing the best database service for your workload, there is no single database fit for everything, this blog post covers the different database options available within Google…

Choose The Right Database Service in GCP.

When it comes to choosing the best database service for your workload, there is no single database fit for everything, this blog post covers the different database options available within Google Cloud across relational (SQL) and non-relational (NoSQL) databases and explains which use cases are best suited for each database option.

There are several factors that we should take into consideration while choosing the right database on Google cloud. These factors include but are not limited to :

Cost-effectiveness

Scalability and elasticity

Type of data; structured or non-structured

Type of database; SQL or NoSQL

Analytics, Reporting, or mobile SDK capabilities, and many more.

The below flowchart will help you understand how to choose the best database as per your business needs keeping in mind all the requirements.

Planning for Database in Google Cloud Platform

Let’s have a look at the brief introduction about these points.Structured VS Unstructured data: Data typically represented in a pre-defined form such as in the form of rows and columns, JSON file, YAML file, log files or any text file is referred to as structured data however, data that doesn't have a pre-defined structure and mostly stored as a static file such as Audio, Video, Images are referred to an Unstructured data.Relational VS Non-Relational data: In relational databases information is stored in tables, rows, and columns, which typically works best for structured data. SQL (Structured Query Language) is used when interacting with most relational databases however, A NoSQL database is optimized for a specific workload pattern (i.e., key-value, graph, wide-column, etc). NoSQL databases are typically faster than SQL databases as they do not scan a lot of tables to deliver the answer which makes it a perfect choice to store data that does not have a fixed schema.Cloud SQL: Provides managed MySQL, PostgreSQL, and SQL Server databases on Google Cloud. It reduces maintenance costs and automates database provisioning, storage capacity management, backups, and out-of-the-box high availability and disaster recovery/failover. For these reasons, it is best for general-purpose web frameworks, CRM, ERP, SaaS, and e-commerce applications.Cloud Spanner: It provides all the relational database capabilities of Cloud SQL along with horizontal scalability which usually comes with NoSQL databases. Spanner is best used for applications such as gaming, payment solutions, global financial ledgers, retail banking, and inventory management that require the ability to scale limitlessly with strong consistency and high availability.Cloud BigTable: It is a wide-column-based NoSQL database for large-scale low latency workloads. It is ideal for a workload that requires heavy reads/writes and high throughputs.Cloud Big Query: It is an enterprise Data warehousing solution for a large amount of relational structured data. It is optimized for large-scale, ad-hoc SQL-based analysis and reporting, which makes it best suited for gaining organizational insights.Cloud Storage: This is a service for storing your objects in Google Cloud. An object is an immutable piece of data consisting of a file of any format such as Audio, Video, Images, Blobs, or any unstructured data.Memory Store: Cloud Memorystore provides a fully managed in-memory data store service built on scalable, more secure, and highly available infrastructure managed by Google. Use Cloud Memorystore to build application caches that provide sub-millisecond data access. Memorystore supports both Redis and Memcached and is fully protocol-compatible. Choose the right engine that fits your cost and availability requirements.Cloud Firestore for Firebase: Cloud Firestore is a flexible, scalable database for mobile, web, and server development from Firebase and Google Cloud. Like Firebase Realtime Database, it keeps your data in sync across client apps through realtime listeners and offers offline support for mobile and web so you can build responsive apps that work regardless of network latency or Internet connectivity.

Conclusion:

Choosing a relational or a non-relational database largely depends on the use case. Broadly, if your data structure is not going to change much, select a relational database. Google Cloud uses Cloud SQL for any general-purpose SQL database and Cloud Spanner for large-scale globally scalable, strongly consistent use cases. In general, if your data structure may change later and if scale and availability are a bigger requirement then a non-relational database is a preferable choice. Google Cloud offers Firestore, Memorystore, and Cloud Bigtable to support a variety of use cases across the document, key-value, and wide-column database spectrum. For more comparison resources on each database check out the overview.

Check out this video to get an overview of these database services and which service to select as per the requirements:

References:

Your Google Cloud database options, explained | Google Cloud Blog

In relational databases information is stored in tables, rows and columns, which typically works best for structured…

sumber : medium.com

Google Cloud Database: The Right Service for Your Workloads

Learn how data is distributed in GCP, what are the main features of top Google cloud database services, and how to choose the right database for your workloads.

February 28, 2021

Topics: Cloud Volumes ONTAP, Database, Google Cloud, Elementary

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, 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.

Image source

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.

Image source

Google Cloud Database Services

GCP offers several Google Cloud database services you can choose from. Below is an introduction to each.

Cloud SQL

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.

sumber : bluexp.netapp.com

How to choose the right Google Cloud Platform Database

Choosing the right GCP Database depends on a lot of factors including your workload and the architecture involved. Today, I’m going to provide you all with an overview of popular Google cloud…

How to choose the right Google Cloud Platform Database

How to choose the right Google Cloud Platform Database To The Cloud and Beyond! I got you, fam!

Tanner Boriack on Unsplash

Choosing the right GCP Database depends on a lot of factors including your workload and the architecture involved. Today, I’m going to provide you all with an overview of popular Google cloud database services, including key considerations when assessing and choosing a service.

Know Thy Database

Google Cloud Platform (GCP) was built to provide an array of computing resources, database services being one of them. Competent and capable of handling modern data, bound with efficiency, flexibility, and great performance, GCP is a hosted platform solution for disseminated data across geography.

When choosing a Google database service, one should consider a lot of things like type and size of data, latency, throughput, scalability, and IOPs, to name a few.

GCP predominantly offers three types of reference architecture model for global data distribution:

1) Single — The simplest of all deployment models, one can deploy databases by creating new cloud databases on Google and/or by ‘lift and shift’ of pre-existing workloads.2) Hybrid — These types of deployments are useful when one has applications in the cloud that need to access on-premises databases or vice-versa.

There are three primary factors to be considered when deploying a hybrid model (with some data on Google Cloud and some on-premises) :

Master Database: First and foremost you need to decide whether your master database is stored on-premises or on the cloud. Once you choose the cloud, GCP resources act as a data hub for on-premises resources, whereas if you choose on-premises, your in-house resources sync data to the cloud for remote use or backup.Managed Services: Available for resources in the cloud, these services comprise scalability, redundancy, and automated backups. You, however, have an option of using third-party managed services.Portability: Based on the type of data store you choose, the portability of your data is affected too. To ensure reliable and consistent transfer of data, you need to consider a cross-platform store, such as MySQL.3) Multicloud — These types of deployments can help you effectively distribute your database and create multiple fail-safes as it enables you to combine databases deployed on Google Cloud with database services from other cloud providers thereby giving you an advantage of a wider array of proprietary cloud features.

There are 2 primary factors to be considered when deploying this model:

Integration: Ensuring that client systems can seamlessly access databases, regardless of the cloud they are deployed on, for instance, use of open-source client libraries to make databases smoothly available across clouds.Migration: Since there are multiple cloud providers, one may need to migrate data between clouds with the help of database replication tools or export/import processes. Google Storage Transfer service is one such tool to help you with database migration.

Cloud is the Limit: Google Cloud Platform Database Services

GCP offers several database services that you can choose from.

Cloud SQL:

A relational GCP database service that is fully managed and compatible with MySQL, PostgreSQL and SQL Server, Cloud SQL includes features like automated backups, data replication, and disaster recovery to ensure high availability and flexibility.

When to choose: From ‘lift and shift’ of on-premise SQL databases to the cloud to handling large-scale SQL data analytics to supporting CMS data storage and scalability and deployment of micro services, Cloud SQL has many uses and is a better option when you need relational database capabilities but don’t need storage capacity over 10TB.

Cloud Spanner:

Another fully managed, relational Google Cloud database service, Cloud Spanner differs from Cloud SQL by focusing on combining the benefits of relational structure and non-relational scalability. It provides consistency across rows and high-performance operations and includes features like built-in security, automatic replication, and multi-language support.

When to choose: Cloud Spanner should be your go-to option if you plan on using large amounts of data (more than 10TB) and need transactional consistency. It is also a perfect choice if you wish to use sharding for higher throughput and accessibility.

BigQuery:

With BigQuery you can perform data analyses via SQL and query stream-data. Since BigQuery is a serverless data warehouse that’s fully managed, its built-in Data Transfer Service helps you migrate data from on-premises resources, including Teradata.

It incorporates features for machine learning, business intelligence, and geospatial analysis that are provided through BigQuery ML, BI Engine, and GIS.

When to choose: Use cases for BigQuery involve process analytics and optimization, big data processing and analytics, data warehouse modernisation, machine learning-based behavioural analytics and predictions.

sumber : towardsdatascience.com