What are the trade-offs between cloud-based and on-premise data architecture patterns?

I would advocate that the concept of "a more complex" environment is a concept which is viewed through the guise of a legacy perspective. In fact a cloud or SaaS architecture allows for a more streamlined approach to multiple facets of a platform. Through the use of microservices, standardized data transformation standards (XML, JSON, etc) and flexibility in open programing and scripting languages a more flexible and simpler approach to nearly every facet of an enterprises architecture can be achieved.

A meticulous evaluation is paramount when moving towards a cloud-based data architecture. Organisations must consider various factors, including their global business presence, data sovereignty, and the shared responsibility model. It's essential to weigh the total cost of ownership, upskilling requirements, and other relevant aspects. Based on my experience, adopting a multi-cloud approach is more appropriate for enterprises with a global footprint. This approach provides much-needed resilience in scenarios where the availability of a specific cloud service provider or required services becomes a challenge.

Regulatory laws and Compliance needs to have data stored with in geography and restrictions on data movements outside jurisdiction are few of the challenges which can be addressed by On-prem data architecture patterns

Consider on-premise solutions as owning a house. It's yours to customize, paint, or renovate. This translates to unparalleled control in data architecture. You decide the security protocols, akin to installing your own security systems in a home. However, the initial investment is hefty, similar to a down payment. And just as a pipe burst or electrical issue would be the homeowner's responsibility, any infrastructure failures need to be addressed in-house, often requiring specialized teams. While you don't have recurring "rent", maintenance isn't free. Over time, as technology evolves, your in-house systems might become outdated, like an old house requiring renovations.

Though on-premise data architecture patterns come up with increased control and customisation, the capital and operational costs associated with dealing the challenges are more. - It offers good control over data privacy, data governance & compliance - It offers low latency for processing pipelines, especially useful for real-time applications. - Ensuring robust security controls towards vulnerability management, exposures and data breaches is costly and often challenging to provision, manage and monitor pipelines proactively. It requires a very high degree skillset and cost intensive. - Elasticity and scaling is a big concern to enterprises. The inherent CapEx model makes it really difficult for enterprises in doing planning and budgeting.

Data gravity in my experience is a huge factor in determining to repatriate back to on-prem. Data gravity is the data that grows in a repo + user growth. That repo is going to have a database, application and user(s). Data gravity will impact all 3. Those costs are significant. Some other advantages on prem; depreciation of assets greater control over data privacy and governance possibility to improver perf through latency (Don't be fooled, on prem does not equal better latency).

To be clear, hybrid means having some resources on-premises, and some in one or more clouds. It can also be that within your on-premise, part of a cloud like Azure Arc or AWS Outpost or GCP Anthos. Before you try this, ask if you can be on cloud or purely on-prem and if there is a plan to go to one exclusively in future. On the other hand, you may have a good reason to stay on hybrid and maintain the different ( possibly different or even diverging ) tech stacks.

A hybrid approach is like owning a primary residence but occasionally renting a vacation home. You get the best of both worlds. For your regular needs, you have your customized home (on-premise solutions). But when you need a quick getaway (scalability during spikes), you rent a place (cloud resources). This approach offers flexibility without compromising on control. However, managing two properties isn't easy. Ensuring seamless integration between on-premise and cloud is like making sure you equally maintain both your house and your vacation rental. If not done right, you might end up with the disadvantages of both worlds, like having a leaky roof in your home and a broken heater in your rental!

Data gravity is a critical consideration in hybrid data architectures. It's essential to understand that data creates a gravitational pull that attracts applications, processes, and services -- strategically placing data is critical to minimising costs, reducing latency, and enhancing efficiency. Organisations can use powerful techniques such as data caching, redundancy, locality, and intelligent data placement to address data gravity. These strategies empower organisations to manage data optimally in their hybrid landscapes, ensuring that data's gravitational pull doesn't hinder performance or cost-effectiveness.

based on my experience , choosing between cloud-based and on-premise data systems, including hybrid options, requires considering various factors. Cloud systems like AWS, Azure, and GCP offer flexibility and cost savings but come with ongoing expenses, data transfer costs, and security concerns. On-premise solutions offer control and potential cost savings but lack cloud's scalability and may entail higher upfront expenses and maintenance efforts. Hybrids aim to balance both but require careful planning and data management. The choice depends on needs, budget, and regulatory requirements.

It's been my experience that there is no one "right answer" to this. Designing a data architecture that accounts for flexibility with data existing in multiple clouds and/or on-premise is the way to protect an organization and be able to respond quickly to the changing landscape. I've seen multiple place where all data was in the cloud and then costs got to be too high, or regulations changed and it was a nightmare to move it all again. Keeping it in multiple places provides the flexibility needed no matter what comes.

I am reminded of this quote by Dmitriy Ryaboy (prev Engg Manager @ Twitter): “Some people, when confronted with a big data problem, think, I’ll use Hadoop. Now, they have a big data problem and a big Hadoop cluster”..... While starting out on the Cloud may be the right choice for Startups or disjoint/disassociated initiatives, trying to manage both an On-Prem and In-Cloud infrastructure can be quite painful and costly. Both have their own pros and cons, and Context is everything. Good luck!

Any architecture is created in support of one or more goals. To take an example, a landscape undergoing cloud modernization could lean towards cloud or hybrid option depending upon the modernization scope. Constraints and complexities associated with the option will have to be managed. In fact, these goals could guide the journey in a direction. It is really about knowing these goals ahead of time. It is also seen, that the complexities and considerations of the current state influence the architecture under the chosen option to certain extent.

In theory, you could argue that there should be no difference between many on-prem, cloud and hybrid architectural patterns. The real differences are concerned with ownership and control; it is the location or security of components that cause changes, sometimes subtle, to the architectures rather than any architectural difference per se. We're not at the level where all cloud services are as interchangeable as microservices with APIs but that is the direction of travel and some architectural decisions (like explicitly adopting a security posture that isn't solely about an external boundary but protects all interfaces) allows changes over the lifetime of the architecture. Build it so you could choose any vendor for any component.

On-premises (NOT on-premise!) compute means buying, shipping, installing, maintaining, and then owning 100% of that compute resource until you physically cart it out of the data center. This can be good if you have a workload that: 1: can actually use most of that compute power 24x7x365 2: can generate enough revenue to pay the datacenter staff to maintain it. Cloud compute lets you pay for only for the minutes you need. Serverless services like Lambda, S3, Aurora, RedShift, etc. take care of getting those compute resources for you, and sell them to someone else the second you don’t need them, so your application costs can get extremely low if you optimize for cost. Which is just not possible on premises.

Multi Cloud Architecture Pattern Multi-Cloud patterns offer a beacon of flexibility and optimization for organizations. With the ability to handpick specialized services from multiple vendors, businesses can distribute risk and enhance performance. However, it's important to note that this flexibility comes with its challenges, particularly concerning the complexities of integration, cost management, and technology oversight. It's crucial to recognize that the Multi-Cloud Architecture pattern cannot fit into the Cloud Architecture pattern. Single Cloud architectures have become increasingly popular, offering a unified technology stack and a straightforward cost structure, placing "all eggs in one basket" and risking business.