December 6, 2022 at 2:44 p.m
Cloud computing has now become one of the leading topics in the field of information systems and big data. In fact, in the age of massive virtualization, the cloud seems more than ever to be a real revelation, allowing companies to become more efficient and more serenely manage the costs related to their information systems.
The term cloud computing is a new computing paradigm based on the delivery of computing services as on-demand services. Cloud computing is accessible from anywhere, anytime and for anyone. This new technology allows companies to offload their data storage and access additional computing power that allows them to process larger amounts of information more efficiently.
Cloud computing is based on the pay-as-you-use model. The resources made available are subject to a fee. In terms of unused resources, they remain completely free. The cloud is therefore of economic importance as its flexibility in the provision and use of its services allows significant savings. The cloud enables economies of scale at a very favorable price-performance ratio.
The term cloud computing refers to the idea of accessing data and services on remote servers. Traditionally, companies use their own infrastructure to host their services; They buy their own servers and then develop and maintain the systems they need to run.
On the other hand, cloud computing relies on remote architectures designed to allow suppliers to ensure continuity of maintenance and service. Cloud computing services are also accessible through web browsers, these services include tools and applications such as data storage, servers, databases, networks and software.
One of the biggest challenges for companies right now is the growing data complexity, since the amount of data to be managed is doubling every year. Enterprise managers must make decisions about integrating new data sources regardless of the type of cloud, SaaS, unstructured data, or routing legacy ERP data to new destinations.
Cloud deployments are generally divided into three types based on their service model or architecture. These two definitions refer to the same concept: “Cloud computing is generally broken down into three service-level offerings, namely SaaS: Software as a Service, PaaS: Platform as a Service, and IaaS: Infrastructure as a Service”.
These three layers support virtualization and management of different solution stacks. IaaS is the most basic layer of a cloud solution in a cloud computing model. Within an IaaS model, a cloud provider hosts infrastructure components such as servers and network hardware. This is an optimal option for companies that want to build applications from scratch and thus generate more control.
With PaaS, you can host your own custom application with a provider that manages everything for you except your application and your data. In this way, you are systematically provided with the infrastructure, operating system, graphical user interface, programming languages, database management and services required to run your application.
In the SaaS model, providers host and manage infrastructure and applications for users. With SaaS, the user does not need to install anything since the software is hosted in the cloud. Buyers have some control over configuring certain settings, e.g. B. designating authorized users or creating custom dashboards.
In addition to the three types of cloud services, there are four types of clouds. The differences between the four cloud types relate to the degree to which resources are shared with other users. Public Cloud: Public clouds are cloud environments typically created from computing infrastructure that is not owned by the end user. Some of the largest public cloud providers use Huawei Cloud.
Huawei Cloud Big Data Services are based on MapReduce Service (MRS), a one-stop big data platform. MRS can connect to data visualization services. Therefore, companies can create a unified big data platform for data access, as well as provide one-click data analysis and storage and value extraction. Huawei helps customers easily migrate data channels to the cloud, design and schedule big data jobs, and view data.
In this way, customers are freed from the complex structure of a big data platform, but also from the optimization and maintenance of big data. In addition, these customers can focus on industrial applications and use the data in multiple service scenarios.
Among the advantages of Huawei Cloud Big Data services is that they include the MapReduce Service, which is fully compatible with the open source Big Data ecosystem. With its many data migration tools and applications, MRS helps companies quickly migrate data from their own platforms without code changes or downtime. Storage and processing power are decoupled so that resources can be flexibly configured and elastically scaled based on service requirements.
In turn, resources can be allocated more accurately and appropriately, greatly improving the utilization of big data clusters. In addition, the high-performance storage-compute decoupling architecture overcomes the old parallel processing limitations inherent in the integrated storage-compute architecture. It optimizes data access efficiency and deep parallel computing (such as metadata operation and write algorithm optimization), while maximizing the high bandwidth and high parallelism of OBS to greatly improve performance.