思考實驗：若 100GB 結構化／3D 資料可無損壓縮至 1GB，你會建構甚麼？

This post is a pure thought experiment exploring what kind of system designs and products would become possible if data size constraints suddenly shrank to 1/100th of what they are today.

​The Problem: Data That Cannot Lose a Single Bit is Getting Too Heavy
​Anyone building or maintaining systems today feels the growing pain of data bloat.

​With images and videos, we can rely on lossy compression to trick the human eye. But when it comes to data where neither a single bit of precision nor the exact order of rows can be altered, we don't have that luxury:
​Nationwide 3D city models and point cloud data used in smart cities and surveying
​Spatial sensor logs recorded by autonomous vehicles and robotics
​Millisecond-level financial tick data and massive transaction histories
​Databases and log files bloated to hundreds of millions or billions of records
​Even with existing compression algorithms,
these datasets easily reach hundreds of gigabytes or terabytes. They drive up cloud storage costs (like S3), query scan fees (like BigQuery), and above all, create a hard physical wall: they are simply too heavy to fit into local machines or standard server RAM.

​The Hypothesis: A World Where 100GB+ Data Becomes 1GB (100% Lossless)
​So, I started playing around with a quick thought experiment:
​What if a technology existed that could shrink 100GB+ of massive numerical, structured, or 3D spatial data down to under 1GB (a 100x reduction)—while keeping 100% of the original row order and precision (completely lossless, with identical SHA-256 hashes upon decompression)?

​Furthermore, what if it had the characteristic where the larger the original dataset, the higher the compression ratio (for instance, shrinking an 800GB dataset down to just a few gigabytes)? How would our infrastructure design philosophy change?

​1. Fitting an 800GB Nationwide 3D Map into Standard Laptop RAM
​Right now, high-precision 3D geospatial data and point clouds are so massive that you need dedicated high-end workstations or large-scale GIS servers just to touch them.
If an 800GB dataset shrank to a few gigabytes, you could load an entire country's 3D map directly into the RAM of a standard laptop or even a web browser environment—with zero loading times. The barriers to building disaster simulations, digital twins, and massive open-world games would collapse overnight.

​2. Keeping 100% of Raw Autonomous Driving and IoT Logs without Downsampling
​When sending data from edge devices to the cloud, we currently have to compromise due to bandwidth and storage limits—recording only anomalies or downsampling to averages.
If data volume dropped to 1/100th, we could stream and permanently store every single raw driving log and sensor history without throwing anything away.

​3. Freedom from DB Archiving and Painful Cleanup Batch Jobs
​We could finally ditch tearful data lifecycle rules like "Keep only the last 3 months in hot storage and delete or banish the rest to cold storage." Years or even decades of raw historical data could remain online and instantly queryable at all times.
​What Data Would You Throw Into It, and What Would You Build?

​When data shrinks to this extreme, it goes way beyond saving on cloud bills—it unlocks features and services we previously gave up on because the data was simply too heavy.
​If a "black box" that losslessly shrinks massive structured and 3D data by 100x suddenly landed on your desk today, what dataset would you feed it first, and what kind of system would you build?
​Whether it's "This would solve a massive bottleneck in my industry" or "It would let me build this crazy architecture," I'd love to hear your ideas in the comments!