In recent discussions, I've emphasized how data products can serve as self-contained encapsulations of analytics. This means that each data product can include models, transformations, and use-case-specific datasets, all bundled together to address particular business needs. This concept leads to a fascinating question about how this approach relates to the current marketing buzz around database performance benchmarks like TPC-DS.

Rethinking Database Performance in the Context of Data Products

Typically, performance benchmarks such as TPC-DS focus on handling massive amounts of data stored in cross-use data structures, like Star Schemas. These benchmarks are designed to measure the efficiency of databases in managing large-scale data across various applications. However, they do not typically address the actual data size utilized in each specific use case, which is where data products can shine.

Use-Case Specific Data in Data Products

A great question from a recent conversation got me thinking: if we were to publish use-case-specific data within each data product, what would the average size be? This is a tricky question because most data isn't typically stored on a use-case-by-use-case basis. Instead, it's often aggregated and stored in more general structures.

To gain more insights, I decided to conduct a poll to gather feedback from the community.

Why Understanding Data Size Matters

Understanding the average size of use-case-specific data can provide several benefits:

1. Optimization: Knowing the data size can help optimize storage and processing resources.
2. Performance: It can offer insights into performance expectations for different data products.
3. Cost Efficiency: Smaller, use-case-specific datasets can reduce storage costs and improve query performance.
4. Customization: Tailoring data products to specific use cases can enhance the relevance and effectiveness of the analytics provided.

Conclusion

The shift towards self-contained data products represents a significant evolution in how we think about data analytics and performance. By focusing on use-case-specific datasets, we can potentially unlock new efficiencies and insights that traditional, large-scale benchmarks may overlook.

Your feedback and insights are crucial as we continue to explore this exciting frontier. Let’s engage in this conversation and refine our understanding of the role and potential of data products in modern analytics.