How can you adapt your cloud data architecture to changing business requirements and avoid obsolescence?

Businesses can gain a variety of advantages from cloud computing. Businesses may increase their productivity, save costs, and become more adaptable by switching to the cloud. Here are some of the most recent cloud computing technologies that businesses can utilize: Serverless Computing Edge computing Artificial intelligence (AI) and machine learning (ML) Blockchain Quantum Computing By heeding these recommendations, Business is utilizing the most recent cloud computing innovations and avoid obsolescence. Cloud computing can provide several advantages that can assist firms in preparing themselves for a range of issues in the future.

In my experience from the mainframe perspective, is that assessing which workloads run best on each platform is a great place to start. For example, there are many workloads that the mainframe will run more efficiently and faster than if they were lifted and shifted to cloud or refactored. This leads to a lot of hybrid mainframe to cloud strategies where fast paced development can happen on cloud for front end pieces and the mainframe remains on the backend as the powerhouse it is.

Understanding business behind the current state is the key. What I’ve found to be helpful is that you find a way to discuss with other businesses that either are at the same status as yours or have already progressed. Based on these discussions you will also get to know your own status and maybe identify things you hadn’t identified before. It is very likely that you don’t have to invent the wheel here and that someone has already been there. Get to know your business and data very intimately and identify your gaps especially. Try to find someone from your organisaation that would help you define your companys risk appetite. There’s a lot of strategic thinking required even in the this phase - the perspectives you need to be assessing.

In small organizations, assessing the current state often makes sense. However, in large matrix organizations, it becomes a time-consuming and complex task. While having a high-level understanding of the current state is helpful, delving too deeply into it can derail the investment and value proposition. I advocate for implementing a minimum viable product (MVP) cloud platform with a target state vision. This approach allows for scalability as we accommodate more business adaptations over time. Additionally, finance modeling for cost distribution, particularly in large matrix organizations, is a critical aspect of data architecture. Security considerations are paramount for any data architecture implementation.

Focus on a scalable and adaptable cloud architecture by integrating microservices, serverless functions, and containers. Adopt a multi-cloud approach to use various services avoiding dependency on cloud vendors. Continuously monitor for efficiency, optimize costs, and keep your tech. stack updated. Employ DevOps and agile methods for streamlined development, and automate resource management and deployment. Focus on data governance and compliance for regulatory adaptability. Design your system for interoperability with open standards and APIs, ensuring smooth integration with new services. Conduct periodic strategic reviews to ensure your cloud architecture aligns with business goals and remains effective in addressing evolving requirements.

I believe that to plan for your future state, you should commit to keeping a strategic mindset. Be adaptable and open minded. Understand your organization's current and future goals. This should help identify areas where current systems may be lacking or where future requirements could demand changes. Clearly define future business goals and objectives that the cloud architecture would need to support. Stay informed on emerging technologies and trends in cloud data architecture, data management, and other related key areas. Aim to reduce complexity by decreasing the number of technologies and vendors within your IT stack to maximize interoperability and scalability, which will further help you adapt to changing needs.

Adapt cloud data architecture with: 1. **Flexibility:** Design for changing data requirements. 2. **Modularity:** Use interchangeable components. 3. **Scalability:** Ensure the system can handle growing data loads. 4. **Standardization:** Follow industry standards for compatibility. 5. **Continuous Evaluation:** Regularly update to align with business needs. 6. **Cloud-native:** Leverage latest cloud technologies. 7. **Data Governance:** Maintain quality, security, and compliance. 8. **Agile Development:** Quickly respond to evolving business needs.

- map business plans, visions and requirements, which always will be a challenge so, put your product owner hat-on. - industry standards, new patterns mapping old capabilities, scalability and availability for challenges - address migration plans, alternate plans, subsume them all opportunities into one future data model - (ignore constraints of migration - work with business plans on them, as it will hurt your designs) - pivot/prepare deployment plans, map future models to existing models, establish traceability, - map future state to current state from data, process, new business opportunities and benefits, new features, cost benefits, operations model, deployment models. resolve gaps while mapping.

Go to discovery mode for different vendors, experts and platforms. You will need to be discussing with many experts to get insights on what is the right platform. Also discuss with 3rd parties that provide services to the platforms you’re interested in to get their insights too. Once again, if you feel that you’re inventing the wheel again - you just haven’t found the right experts or partners - so keep looking and learning.

To choose the right cloud platform for your cloud data architecture: - Understand data requirements and use cases such as storage, processing, analytics, etc. - Assess compatibility and integration with existing systems - Assess scalability, durability, and cost of storage options. - Evaluate data processing capabilities. - Assess analytics and machine learning services. - Assess security features and compliance. - Understand pricing for storage, processing, and additional services. - Review vendor support, SLAs, and commitments. - Consider community size and ecosystem support. - Consider data transfer speed, latency, and associated costs. - Conduct a hands-on pilot to validate suitability for use cases.

As multicloud environments become increasingly more commonplace, organizations can gain a competitive advantage by leveraging the strengths of each provider for each specific use case. Leaders must enable and foster growth in multicloud architectural patterns that leverage streamlined and scalable Business, Design and Operational agility.

There is no right cloud platform.. all cloud decisions so far are politically driven, and decided by people who have no considerations to architecture and design. So, ensure all your functional models can be executed in all the cloud available platforms. Start modeling for changing from one cloud to another, and prepare multi-cloud plan. Find which cloud provides the best capabilities in terms of ease of operations, reduced operational costs (remember this is where you are moving from CAPEX to OPEX), and ensure there are no roadblocks in your architecture to execute your design.

Some design principles and good practices can help to get a flexible and modular data architecture: - manage raw data in common format: you can work it easier. - guide all process with metadata as possible: automatize the work of process and reuse logic. - work all storage based in one singles focus based layers or components: you can reuse data to difference goals in next layer. you can align and use your tools; Spark or block storage like S3 for example ; to support this focus development. Remember these are part of design your architecture.

The approach can be one of a docking station where different vendors can use abstracted data formats hiding the underlying secret sauce of the API implementation. Then customers can compare vendors of the implementation against dimensions like scalability, performance, reliability, cost, flexibility of use, lifecyle stage of the API, etc., to choose one vendor's implementation over the other

Designing for flexibility and modularity is a cornerstone principle in adapting cloud data architecture. Avoiding rigid, monolithic solutions, and opting for modular or microservice-based approaches fosters scalability and ease of modification. Embracing flexible, abstracted data formats and interfaces accommodates diverse or evolving data sources. This design philosophy enhances the agility and adaptability of cloud data architecture, allowing for seamless scaling, modification, or replacement of components. In a rapidly evolving technological landscape, prioritizing flexibility and modularity ensures that your data architecture remains dynamic, responsive, and capable of meeting the evolving demands of your organization.

A holistic approach in living between legacy monoliths and microservices is often easier said than done. Nonetheless, with a microservice first approach many organizations can find themselves much more agile while reconciling their monolith technical debt. Of course, with more microservices coming into fruition an updated operating model on the governance of both the monolith and microservice is a must.

Always! no matter going cloud, local, mobile app or standalone services. Always design for flexibility, modularity, as we all have learned by GoF design patterns. Microservices, Mesh and most modern problems are because poor data models, product vision, and understanding of the solution in the design. Even many AI/ML training data preprocessing is unnecessary if had done your data models right, functionally decomposed to business problem. Don't forget this, but then at EOD, it is your decision for the given problem context. Extreme programming, refactoring way is the ideal way to move towards cloud data architecture. Use the Architecture driven methods to validate architecture every release.

APIs and Integration: Build your architecture with well-defined APIs to facilitate integration with new systems and technologies.

Data platform boils down to 5 key building blocks: data source, ingestion pipeline, storage, data processing and consumption interfaces. Make these building blocks generic and parameterize them. You can use technologies such as Infrastructure as Code (IaC) and DevOps pipeline to automate and scale the provisioning of built-for-purpose data platform components.

Automation is becoming a big part of software development. Using Hybrid cloud tools such as OpenShift which have native CI/CD tools, a large proportion of the solution creation, container orchestration, and deployment can be automated.

Orchestration sometimes can hurt the development if it’s implemented in a wrong way. Make sure your enterprise has the right people to manage your distributed system. Also security can still be a concern when using orchestration tools like K8s. For automation, consider using IaC to streamline your cloud operations.

With the beauty of automation and orchestration, there are often challenges in the enterprise in regards to the delineation of responsibilities between teams.? Properly designed and well governed enterpise and architectural patterns are a must. Engineers and architects of every sort should adhere to the global policies and patterns to fully optimize automation.

Regular Assessments: Conduct regular assessments of your data architecture to identify areas that may need improvement or updating. Stay in tune with industry trends and benchmarks to ensure your architecture remains competitive and aligned with best practices.

We need to work with a living architecture as it evolves. This is at various levels of the TOGAF/Zachmann frameworks as well as the data, application, and conceptual architecture layers. Flexibility in architecture is a sub goal

Training and Skill development Invest in the training and skill development of your team. Ensure that your team is knowledgeable about the latest technologies and best practices in data architecture. A skilled and informed team can better navigate changes and make informed decisions.

1. Adopt a Modular and Scalable Architecture: ? Serverless Computing: Leverage serverless platforms (e.g., AWS Lambda, Azure Functions) 2. Utilize Cloud-Native Tools and Services: ? Managed Services: Use managed services (e.g., AWS RDS, Google Cloud BigQuery) to reduce maintenance overhead. 3. Enable Continuous Integration and Continuous Deployment (CI/CD): ? Automated Pipelines: Implement CI/CD pipelines to automate testing and deployment, ensuring quick adaptation to changes. 4. Embrace Infrastructure as Code (IaC): ? IaC Tools: Use tools like Terraform, AWS CloudFormation, or Azure Resource Manager to automate infrastructure provisioning and management. 5. Focus on Security and Compliance: ? Automated Security.