In the Web3 space, when a new idea sprouts, the founding team often quickly assembles and dedicates themselves to advancing the project. However, almost all teams soon face a common challenge: data processing.

This issue often requires a significant investment of resources, including hiring expensive data engineers and even forming dedicated teams to build and maintain nodes, index blockchain states, and ensure the stability of APIs. This involves not only obvious salary expenses but also hidden costs such as server fees, operational pressure, and the diversion of the most valuable core team energy.

For a startup project that pursues rapid development and efficiency, such resource allocation can become a fatal weakness. Top programmers, who should focus on developing unique product features, may be exhausted dealing with underlying technical issues.

However, we can take a different approach now. Instead of hiring a high-salaried individual data engineer, we might consider leveraging mature, stable, and cost-effective platform services. This 'data engineer as a service' model can respond to demands at any time, never gets tired, and has unlimited scalability.

Choosing such a service platform is equivalent to immediately gaining the support of a world-class SRE (Site Reliability Engineering) team. They can handle all the tedious underlying tasks, allowing the startup team to focus all their energy on business innovation. More importantly, this approach can transform the originally high fixed labor costs into predictable, scalable operational costs that grow with usage.

In the fierce competition of Web3 entrepreneurship, how to efficiently manage data resources may become a key factor determining the success or failure of a project. By wisely choosing data processing solutions, entrepreneurial teams can better cope with the challenges brought by rapid growth, focus on the development of core business, and stand out in this field full of opportunities and challenges.