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The Rise of Cloud Data Architecture

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The rising popularity of cloud data architecture signals a central theme: Global businesses in the coming years will migrate their data center operations fully to the cloud, as it offers some inherent advantages over on-premise set-ups. Business survival in the digital age has become synonymous with migration to the cloud.

When businesses look for unlimited data storage and exceptional computing capabilities at an affordable cost, they turn toward cloud data architecture for enterprise Data Management. 

Businesses that want greater control over their data create a custom, in-house private cloud service. Private and public clouds are complex distributed systems, which are better with an application architecture that breaks down the processing and data into distinct components.

Affordable Data Access of Cloud Data Architecture

Cloud data platforms offer affordable access to a wide array of Data Management resources, such as servers, storage, applications, and services, which can be quickly deployed and used without any interaction from the service provider. 

Cloud data architecture contains rules, policies, and models for data acquisition, storage, analysis, and management in a cloud-based environment on behalf of a company or organization. Moreover, modern cloud architectures separate the computing layer from the actual hardware, thus making it easy for end users to interact with the cloud environment through a front end. 

The storage component of cloud computing provides the storage capability on the cloud to store and manage data. Third-party cloud storage providers such as Amazon’s Simple Storage Service (S3), Microsoft Azure, and Google Cloud Storage are capable of managing and maintaining data, as well as providing remote backups. 

The virtualization software separates the data storage and compute layers from the hardware layer in the cloud, and users interact with the cloud infrastructure via a graphical user interface (GUI). Virtualization, by separating the hardware and software layers in the cloud, makes it easy for the cloud providers to efficiently manage all types of software, whether application software or system.

The cloud environment also handles data security events efficiently by automating response generation. Data security can be improved when data is processed and managed using cloud services and by following recommended practices. 

Advantages and Challenges of Data Architecture in the Cloud

The biggest advantage of a cloud data architecture is that it offers unlimited data storage and powerful computing capabilities for all complex big data projects. Well-known cloud providers like Amazon Web Services, Microsoft Azure, or Google Cloud Platform offer transformational big data management capabilities for businesses of all shapes and sizes. 

Most global businesses that have already invested in a cloud data architecture have reported that the advantages they have gained have been somewhat diluted by technical challenges while making the transition. Data architects usually face challenges during rollouts of cloud data architecture environments. Some of these technical challenges include data gravity, data security issues, existing investments, data quality and compliance requirements, and incomplete data migration. For example:

  • Data gravity issues surface in multi-cloud environments during data ingestion, transformation, and analysis stages. Separate data stores, event-driven architectures, edge computing, and batch processing usually mitigate these challenges.
  • Data security challenges surrounding the control, classification, and lifecycle of data are countered by cloud data architecture through the use of strong data security and Data Governance policies.
     
  • An existing investment will always compete with a new cloud data architecture plan. The best way to mitigate this challenge is to validate the usefulness of a cloud data architecture with an actual business case outlining detailed cost-benefit analysis.
  • Data quality and regulatory requirements can pose serious threats to cloud data environments, but there are enough use cases to prove that cloud data architectures have provided sufficient liability protections.
  • Incomplete data migration is a challenge, and many businesses have unfinished data-migration projects due to one reason or another. 

The Secrets of Building a Successful Cloud Data Architecture 

A technical discussion about building a cloud data architecture is beyond the scope of this article, but while planning, a data architect should keep the following considerations in mind:

  • A solid proposal or a business case can be used as the starting point of a cloud architecture plan. It becomes easy to draw up an effective architectural plan if a specific use case is available. With a business use case at hand, the enterprise team can align the needs of the case with selective components of a cloud data architecture. A plan propelled by actual business application has a higher chance of winning the approval of the top business executives.
  • As the cloud architecture paradigm is wide and open-ended, the numerous permutations, unending available technological options, and the wide choice of hardware and software platforms can be intimidating. It is certainly more pragmatic and cost-effective to pilot-test a few scenarios before jumping into an uncertain and costly environment that may not deliver the intended results.
  • Unstructured data management has always been a serious challenge to an enterprise. The cloud data architecture environment offers possibilities for experimenting with unstructured data. Whatever could not be done in traditional database environments can be done now.
  • The focus should be on streamlining data workflows, as they are more important than Data Management technologies and tools. In the long run, this approach will benefit data analytics applications.
  • Cost vs. performance metrics at the outset can end up saving a lot of operating costs, which add up very easily in the cloud. Data Management should be a tiered practice, so that the costly resources are reserved for only a few processes.

The Cloud Service Delivery Options: How They Impact Data

In a Platform-as-a-Service (PaaS) model, the cloud provider provisions the OS, middleware, and runtime, along with the hardware layers – the servers, the network hardware, virtualization capabilities, data, and also the software layers.

In the Infrastructure-as-a-Service (IaaS) model, user roles consist of managing applications, middleware, and operating systems, whereas the cloud service provider takes care of hardware, which includes servers, hard disks, data storage, etc.

The Software-as-a-Service (SaaS) service model allows cloud provider to install and maintain software on the cloud, while users access the software from their desktops via internet. 

Public cloud providers use the “multiple-tenant” model, which basically means renting out the same server spaces for storage and computing services to multiple customers to reduce cost per customer. In this scenario, the same servers are used to deliver services to more than one customer. 

In private cloud deployments, organizations generally use dedicated servers for data storage and computing needs. They typically have higher volumes of transactions and would benefit from dedicated private cloud deployments. They enable integration of applications, whether they are in the cloud or on-premises, and they enable data flow between them without interference. Customers have the tremendous flexibility to access cloud-hosted data and then integrate the data with other data or applications deployed within the same environment. 

Image used under license from Shutterstock.com

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