Here to bring you the latest news in the Cloud is Stephanie Wong.
A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.
Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.
By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.
However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.
Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.
Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.
Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.
Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.
The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.
For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.
#cloud computing services #all #hybrid cloud #cloud #multi-cloud strategy #cloud spend #multi-cloud spending #multi cloud adoption #why multi cloud #multi cloud trends #multi cloud companies #multi cloud research #multi cloud market
If you looking to learn about Google Cloud in depth or in general with or without any prior knowledge in cloud computing, then you should definitely check this quest out, Link.
Google Could Essentials is an introductory level Quest which is useful to learn about the basic fundamentals of Google Cloud. From writing Cloud Shell commands and deploying my first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.
Let’s see what was the Quest Outline:
A Tour of Qwiklabs and Google Cloud was the first hands-on lab which basically gives an overview about Google Cloud. There were few questions to answers that will check your understanding about the topic and the rest was about accessing Google cloud console, projects in cloud console, roles and permissions, Cloud Shell and so on.
**Creating a Virtual Machine **was the second lab to create virtual machine and also connect NGINX web server to it. Compute Engine lets one create virtual machine whose resources live in certain regions or zones. NGINX web server is used as load balancer. The job of a load balancer is to distribute workloads across multiple computing resources. Creating these two along with a question would mark the end of the second lab.
#google-cloud-essentials #google #google-cloud #google-cloud-platform #cloud-computing #cloud
Google Cloud Services
Google Cloud has been developing around the world. The explanation is the broad array of services it provides to its users:
VPC: In order to create a protected environment for your deployments, Virtual Private Cloud provides a pleasant server with IP allocation, routing, and firewall security policies. network and its data centers.
Cloud Load Balancing: It is a mechanism in which workloads are spread across several computing resources.
Content Delivery Network: This lowers costs and maximizes the supply of funds. A geographically dispersed proxy server. The aim here is to include low availability and high efficiency through the spatial delivery of end-user services.
Cloud IoT Core: It is a completely managed service that allows you to link, manage, and consume data from devices that are connected easily and safely. It enables other Google Cloud services to be used to store, process, analyze, and visualize real-time IoT data.
Cloud IoT Edge: Edge computing brings memory and computing power closer to the place where it is required.
Google App Engine: Framework of deployment of Java, PHP, and other applications as a service. It is a platform for Cloud Computing to build and host web applications in data centers operated by Google. It provides the automated scaling function, that is the App Engine automatically allocates more resources for the application to manage the growing demands as the number of requests for an application increases.
Compute Engine: Infrastructure as a Service to operate virtual servers running Microsoft Windows and Linux. This is a Google Cloud platform element that is based on the same infrastructure that operates the search engine, Youtube, and other services from Google.
Kubernetes Engine: It helps to provide a platform for the automation of application container deployment, scaling, and processes through host clusters. It operates with a broad variety of equipment for containers, like dockers.
BigQuery: The BigQuery Service from Google is a completely managed data analysis service that allows organizations to analyze big data. Highly scalable data storage, the ability to conduct ad-hoc questions, and the potential of sharing data insights through the web are also included.
Google Cloud Datastore: A schema-less, non-relational, completely controlled datastore. It provides atomic transactions and a vast collection of query capabilities and can automatically scale up and down depending on the load.
Google Cloud Dataproc: Spark and Hadoop services for distributed data processing are quick, easy to use and manage. You can build Spark or Hadoop clusters, configured for your tasks exactly when you most need them, with Cloud Dataproc.
Identity and Security
Cloud Data Loss Prevention API: This helps you handle classified details. For sensitive information items, such as credit card numbers, names, passport numbers, and much more, it provides a simple and scalable classification.
Cloud IAM: Cloud Identity and Access Management relate to a policy and infrastructure system to ensure sufficient access to technology services by the right people in an enterprise. It is also called control of personality.
Google Stackdriver: Provides data on results and diagnostics in the form of tracking, monitoring, tracking, recording of errors, and alerting it to public cloud users.
Google Cloud Console App: A native mobile application that enables customers to manage the key Google Cloud services. This offers analysis, modification, and opportunities to gain action on resources.
Cloud Machine Learning Engine: A managed service, which will allow you to create models of Machine Learning based on mainstream platforms.
Cloud AutoML: A Machine Learning service that allows developers, through Google’s transfer learning and Neural Architecture Quest, to provide their sets of data and gain access to quality trained models.
Google Cloud Storage: A web service for online file storage to store and manage data on the framework of the Google Cloud Platform. The service blends Google Cloud’s flexibility and efficiency with sophisticated security and sharing capabilities.
Cloud SQL: AA web service that lets you build, manage, and use Google Cloud-based relational databases. It helps you to concentrate on your software and services by managing, maintaining, and handling your databases.
Cloud Bigtable: A NoSQL database service that is quick, fully controlled, and easy efficient. It is intended for data collecting and retention from 1 TB to hundreds of PB.
Google Cloud has been one of the largest cloud providers in the IT industry. Software developers can access the services they provide, because they provide a stable and highly scalable platform for building, testing, and deploying their apps. If you are looking for Google Cloud Training in Chennai and check out FITA Academy Google Cloud Online Training to get ahead in your best career!
#google cloud #google cloud online training #google cloud training in chennai #google cloud course in chennai
In this Google Cloud Training live session, you will know everything about google cloud from basic to advance level with a hands on demo on google cloud to help you understand the concepts better and lastly interview questions which makes you interview ready.
Why Google Cloud Platform is important?
Cloud computing has grown massively and is poised to grow likewise and on-premise infrastructure will essentially have no future. A few vital players have developed in the cloud computing circle, including Amazon Web Services (AWS), computing behemoth IBM, Microsoft Azure and Apple’s omnipresent iCloud. With such alternatives, why are organizations like 20th-century fox entertainment, dominos, HSBC, Bloomberg, Paypal and Twitter are moving their workloads to Google Cloud Platform? You will learn the reasons. The Intellipaat’s google cloud tutorial is easy to understand, has real world GCP examples and thus makes you understand why GCP is so important and why you should go for a GCP Career.
#google cloud training #google cloud platform course #google cloud for beginners
In this Lab, we will configure Cloud Content Delivery Network (Cloud CDN) for a Cloud Storage bucket and verify caching of an image. Cloud CDN uses Google’s globally distributed edge points of presence to cache HTTP(S) load-balanced content close to our users. Caching content at the edges of Google’s network provides faster delivery of content to our users while reducing serving costs.
For an up-to-date list of Google’s Cloud CDN cache sites, see https://cloud.google.com/cdn/docs/locations.
Cloud CDN content can originate from different types of backends:
In this lab, we will configure a Cloud Storage bucket as the backend.
#google-cloud #google-cloud-platform #cloud #cloud storage #cloud cdn