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Guidelines that you can use to help you strike a balance between making revolutionary investments and preserving the most valuable aspects of what you already have.
A data-driven workload placement strategy that prioritizes expenditures based on the impact they have on the business and their level of practicality can help you maximize the success of your cloud transformation.
The vast majority of businesses have already completed the relatively painless transition to the public cloud, typically for applications with fewer dependencies, lower data gravity, or less stringent security, performance, or governance requirements than other apps. However, once they have completed those simple migrations, they encounter opposition.
After these early adopters, the cost, risk, and technological viability of moving to the public cloud may become insurmountable. Bringing the cloud experience to those workloads is the better option, as determined by goals such as cost reduction, improved performance and availability, establishing a more robust security posture, and risk mitigation.
However, even in the early stages of the modernization and migration processes, some businesses are unable to move forward. If you do not start a cloud-based company from the beginning, free of any legacy applications, you may find yourself operating with two different cost and operating models.
And the obvious truths emerge as monthly invoices for cloud services and legacy IT pile up, increasing the total cost of ownership.
Because of the amount of work involved, the length of time required, and the opportunities lost, large-scale portfolio modernization and migration projects can have a significant impact on an organization.
How can businesses avoid straddling both the old and the new to achieve complete digital transformation? Is it possible to predict what will happen if they change their course of action and strategy? Is it possible for businesses to achieve this while also achieving success with all of their business apps and maintaining the same level of urgency that drove the initial rush for cloud experiences?
Because of the amount of work involved, the length of time required, and the opportunities lost, large-scale portfolio modernization and migration projects can have a significant impact on an organization.
To achieve the goal, it is necessary to work hard to completely discover, eliminate, and distill what is known as “noise,” to maintain accurate, real-time data throughout each stage of the lifecycle journey, to select the appropriate priorities, to generate momentum for the change, to leverage the appropriate technologies, and to assemble and motivate an outstanding workforce.
Make sure you understand the relationships between your applications and your IT services
The discovery of the business is the first step toward achieving this comprehension. A necessary step in this process is to develop a high-level perspective of what the business requires to function, who owns what, how much capacity and opportunity for development you have, and how business services are supplied.
This procedure should not be focused on which vendors, infrastructure, hardware, and software to use. Instead, it should specify which activities the business requires from IT services to function, including the volume required as well as the ability to scale up or down depending on the circumstances.
After you’ve completed the business discovery process, you can move on to the application discovery process, which involves mapping various apps to various business services. At this point, you will gain more detailed visibility into the service-level agreements (SLAs) associated with a workload or application, as well as the workload’s relationship to the business’s value chain.
The goal is to record and investigate all aspects of the application, such as its owners, functional areas, lifecycle information, development or feature timeframes, and roadmaps. You’ll also need to understand how each functional component assesses the value of the assets it possesses on an individual level. Surprisingly, many businesses do not recognize the importance of integrating all of these components until one of their most critical business services is discontinued.
Containerization and distributed microservices are used
Containers, microservices, and other cloud-native practices are quickly overtaking traditional software development practices. They are widely used for a variety of reasons, including efficient data management and the state that they provide. Stateless applications do not read or store information about their current state.
Statelessness provides several benefits to microservices deployed inside containers, including scalability, security achieved through isolation, continuity, and a reduction in deployment time. However, because enterprise applications typically need to retrieve, process, and store data, statelessness is not an issue for simple web apps.
Containers are used in the development of modern enterprise applications. Containers start up, perform their tasks, isolate any runtime issues, and then shut down.
To accomplish this, meticulous coordination with a multilayered infrastructure and multiple software services is required. When any application can receive data from one microservice in persistent storage, perform an action on that data, and then pass the results to another microservice, the complexity level rises.
During the same time periodperiod, enterprise DevOps teams are rapidly expanding, as is their need for storage space. As monolithic programs are refactored and new apps based on microservices are designed and deployed, containers are being used to run an increasing number of stateful workloads.
Because all of the disparate hardware and software elements are now coming together and usage is rapidly increasing, persistent storage support for containers is an important topic that deserves to be addressed. Containers and storage must coexist in your environment or else your entire IT operation will suffer. If they do not, the entire IT operation may suffer.
The good news is that both persistent storage support and containerized app state management have seen significant improvements in recent years and will most likely continue to do so. If you use container management and microservices technologies that enable persistent storage and statefulness, you can bring cloud agility to enterprise apps while controlling complexity and lowering risk. These are known as “persistent storage and statefulness support” technologies.
Take note of your data strategy
When integrating the cloud experience with your existing applications and data, the location of your data storage must be considered. There’s a good chance that some of it is in the cloud, possibly in multiple clouds, and that some of it are on company computers in organized, unstructured, and semi-structured forms. Some of the data may have multiple copies. Because giving users access to the original dataset would be too dangerous, administrators may make copies of it or divide it up into subsets.
This complexity, caused by the lack of a comprehensive data strategy, poses a risk to businesses because it jeopardizes service level agreements (SLAs) with customers and partners. When useful but time-consuming programs like machine learning and massive, analytical queries are used, the affected company is unable to guarantee that scheduled events will start and end on time.
A comprehensive data strategy, on the other hand, enables the operation of a multipurpose system that fully exploits the value of data, bringing useful applications (projects) into production as soon as possible.
This allows it to be done in a way that is both affordable and practical. Analysts, developers, and data scientists can work with a comprehensive and consistent set of data, as well as integrate new data sources without breaking the bank or overburdening IT. To do so, a data fabric must have the following essential capabilities:
A single, consistent global namespace must be used to access all data, regardless of whether it is stored locally on-premises, publicly in the cloud, or delivered locally at the edge. This is necessary.
Various data formats and protocols: The data fabric must support a diverse set of protocols, data formats, and open APIs, such as HDFS, POSIX, NFS, S3, REST, JSON, HBase, and Kafka. Furthermore, the data fabric must be capable of supporting multiple file systems.
Automatic policy-based optimization necessitates the provision of a method for the business to determine where data is stored and at what temperatures it is kept, namely hot, warm, or cold.
A rapidly scalable and distributed data store is required because an organization’s data requirements can rapidly and dramatically increase; the data fabric must enable this growth rather than stifle it.
Multi-tenancy and security necessitate consistent authentication, authorization, and access control regardless of the location of the data or the type of system it runs on. This is required for multi-tenancy.
Resiliency at scale necessitates that the data fabric delivers instant snapshots even when the system is heavily used and that all applications have the same view of the data when the snapshots are taken.