Sharing massive databases publicly leads to instant server crashes from connection overload and unoptimized queries, while forcing users to download the data is too slow for quick exploration.
Sharing and exploring massive datasets (billions of rows) is extremely difficult due to the high resource costs of self-hosting, the risk of server crashes from unoptimized public queries, and the challenge of verifying data quality and freshness.
I uploaded 5.6 billion TikTok videos metadata to Hugging Face and giving away access to my database
Two hundred DM'd credentials will kill that box long before any query does.
commentTwo hundred DM'd credentials will kill that box long before any query does. Make one read-only ClickHouse user with a query timeout and a memory cap, post it in the thread, keep the heavy pulls on the public dataset.
if its a one-time scrape thats interesting for research but way less useful for anything production-facing.
commenthow fresh is the data? if its a one-time scrape thats interesting for research but way less useful for anything production-facing. ongoing updates would be a different story entirely
Individuals and small teams hosting massive datasets who need to grant public query access without their infrastructure collapsing.
Recurring warnings that exposing databases publicly leads directly to connection exhaustion, requiring manual intervention.
Unlike Hugging Face (which requires downloading heavy files) or direct DB sharing (which crashes), DataProxy acts as a secure, auto-scaling firewall specifically built for public data exploration.
A managed proxy layer that wraps any standard database (Postgres, ClickHouse) in a secure, connection-pooled, query-limited endpoint, allowing safe public querying without exposing the raw infrastructure.
Hosting a DB robust enough for unoptimized public access costs hundreds of dollars monthly. Users are currently performing manual, unscalable credential management via DMs just to mitigate these costs and crashes.
“Share billion-row datasets publicly without crashing your server.”
A managed proxy layer that wraps any standard database (Postgres, ClickHouse) in a secure, connection-pooled, query-limited endpoint, allowing safe public querying without exposing the raw infrastructure.
Target data hoarders on Hacker News, r/datasets, and independent web scrapers sharing high-value projects.
Parsing, sanitizing, and limiting SQL for different database engines (e.g., Postgres vs ClickHouse) safely is technically complex.
Adding a proxy layer to billion-row queries could introduce latency that defeats the purpose of fast, real-time exploration.
Many data creators want to share for free and may balk at paying a SaaS fee to give their data away, shrinking the TAM to commercial data providers.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis idea scores in the upper-middle range of opportunities surfaced by MonetScope, with a validation sub-score of 8/10 against 3 independently sourced evidence signals. A "promising" rating usually indicates a real pain has been detected and discussed in the open, but the pipeline did not find enough signal to flag it as urgent or high-frequency. These opportunities can still produce excellent businesses — they often correspond to "boring" problems that established players have ignored — but the founder should expect a longer customer-development cycle to confirm willingness to pay.
It sits at the intersection of "api", "data-management", "data-scientists", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
