You're seeking scalable data architecture solutions. How do you manage cost considerations effectively?

The framing for this isn't great because although I've done capacity planning for some of the largest services on the internet and cost is very important at scale, the critical part here is designing for the problem you have. Presumably this is early in the lifecycle, in which case you should burn money and even some complexity on an awareness that you probably do not understand how your customers will use your product. Buy flexibility to adapt to changes instead of optimizing for cost. Saving money to just barely meet your expected requirements is the most expensive solution. Users?!?! Amiright?

Institute a policy where half the budget left over at the end of the year while still meeting SLOs goes to the off-site budget of the platform team.

In my experience, managing costs for scalable data architecture starts with choosing the right tools and technologies wisely. I carefully evaluate different options, considering not only their scalability but also their pricing models. I look for solutions that offer flexibility, allowing us to scale up or down based on demand, and avoid long-term commitments that could become costly if our needs change. I also explore open-source alternatives and cloud-based services that offer pay-as-you-go pricing, ensuring we only pay for what we use.

What is the value add you are providing? Do that. If you don't plan to be innovative and excellent at something, rent it. If you plan to make some small tweaks or push something hard and you are willing to pay in $ and time, consider open source. If you plan to do anything novel, you probably have to write that yourself. If you get successful, figure out what your most expensive/critical thing is and bring that in house. Repeat.

In my experience, designing for efficiency is crucial to managing costs in a scalable data architecture. I prioritize streamlined data pipelines that minimize unnecessary processing steps and data transfers. I also optimize storage by choosing the right format and compression techniques for different types of data. Additionally, I regularly review and tune queries to ensure they're running efficiently, avoiding unnecessary resource consumption. By building efficiency into the design from the start, we can scale our data architecture without breaking the bank.

By all means, look for ways to save money. But at the same time you need to consider the context of your design around the value it provides not just what it costs, which is the bane of having people focused on cost reduction. Every change you make has a cost. Sometimes you spend that effort to reduce costs. Sometimes it is about improving reliability, and sometimes it is focused on new features or reducing tech debt. The important thing is to balance all of those changes in the greater context of the business otherwise you safe going to save pennies in costs and dollars in revenue. The caveat is optimizing so you can survive growth, that's pretty much always a win.

Begin by conducting a thorough cost-benefit analysis to understand the financial implications of various solutions, considering both initial setup costs and long-term operational expenses. Prioritize cloud-based solutions that offer flexible pricing models, such as pay-as-you-go or subscription-based plans, which can scale with your needs and help control costs. Leverage open-source technologies where appropriate to reduce licensing fees while maintaining functionality and performance. Optimize data storage and processing by implementing data lifecycle management practices, ensuring that only essential data is retained and processed. Regularly review and renegotiate contracts with vendors to ensure competitive pricing and favourable terms.

The cost of DR needs deep analysis. Doing region to region data replication can be very expensive. Azure, owning much of their network, seems to have the upper hand over AWS cost models for large scale data replication for BCP