Data, Data Management, Cloud Computing
August 3, 2021
Introduction – The BIG data problem
In the current world of analytics, data scientists, data analysts, developers, and more are presented with business challenges that involve large-scale data. Common challenges like real-time statistics and storage of structured and unstructured data could easily require new data servers, different software, and technical expertise to set up. It may be necessary for data to undergo data transformations or be optimized for a certain file type such as CSV, JSON, Parquet. As data is rapidly changed, updated, and deleted, the data management process must adapt to occur as quickly and cost-effectively as it can. With all the possible questions that arise with business data, cloud computing platforms provide a business-friendly solution. Cloud computing allows employees to manipulate, access, and configure data online.
Optimize Data Access and Storage
Situations arise where the business’ processing power must expand. Businesses need to be able to increase their processing power to meet growing demands. This is known as Scalability and can be performed either horizontally or vertically. Processing power can be enhanced vertically (by adding RAM or CPUs) or horizontally (by adding machines). Adding processing power reduces the chance of overloading the servers with activity, either from the consumers or from internal business use.
In a traditional scenario that would mean the technical team and finances would collaborate to purchase materials and integrate them into the existing data server setup, incurring costs per added machine and extra labor. Overall, the overhead cost of purchasing, provisioning, licensing, securing, and maintaining hardware are fundamental arguments in favor of the cloud. With a Cloud Service Provider, an existing data center can be migrated to the cloud and adapted as needed. Infrastructure as a Service (IaaS) is a type of cloud provider service model dictating what resources are online. A business can use an IaaS cloud platform solution for comparably lower costs than changing an on-premises data center.
Go Further - Improve Data Management Services
Cloud computing service models provide advantages over local datacenters with the ability to scale resources on-demand and to offer compute services that can manage and use data for statistical insights. Common compute services available with IaaS are virtual machines and containers (Kubernetes), these are virtualization services that assist by scaling out applications quickly and efficiently.
Platform as a Service (PaaS) provides a managed hosting environment for the cloud provider to deploy services and applications. Offloading the software and hardware requirements to the cloud provider means the business will not manage deployments, updates, or installing underlying software. Platform as a Service solutions can be combined into an ETL process (Extract, Transform, and Load) for the data to aid business analytics and machine learning with built-in processes for automation. App services such as Microsoft Azure’s Logic Apps are included in PaaS, allowing the business options to connect in databases and other useful API controllers.
With Software as a Service (SaaS) solutions, the cloud provider manages all aspects of the environment including storage, network resources, applications, and machines. An example of a well-known SaaS solution is Microsoft Office 365. SaaS solutions are prebuilt to be turn-key solutions, only requiring configuration to manage the service.
The business scenario may vary, but basic elements of data management will always present challenges with cost-effectiveness and optimization. Implementing a cloud computing provider platform in a business can make a positive impact on storing, managing, and accessing data. The cloud computing service models IaaS, PaaS, and SaaS offer increased levels of flexibility for cloud provider management as opposed to traditional on-premise datacenter maintenance and improvement.
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