Cube – open-source headless BI

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Hi HN, I'm Igor, a proud part of the team here at cube.dev. I'm excited to share some news about Cube, an open-source headless BI platform that now has almost universal connectivity to both upstream and downstream tools in the data pipeline: https://cube.dev/blog/expanded-bi-support If you're confused by "headless BI", there's a blog post to get you covered (https://cube.dev/blog/headless-bi), but the most concise and developer-friendly way to put it would be that Cube can connect to any data source that speaks SQL (from Postgres to BigQuery to ClickHouse to Materialize) and expose your data, conveniently presented as high-level metrics rather than in terms of tables and columns, via a set of APIs, including REST, GraphQL, and SQL API. The last one is particularly interesting, because it gives you an opportunity to deliver metrics through Cube to notebooks like Jupiter or BI tools like Tableau or Superset (all covered by SQL API), just like you can deliver them to front-end applications (covered by REST/GraphQL). Do you see Cube fit for the applications you're building? Let's discuss here or in Cube's Slack community (https://slack.cube.dev) where we recently celebrated that Cube has got over 13,000 stars on GitHub (which is not that bad for a developer tool, I guess).

Visit product View on Hacker NewsMay 24, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, new, apis · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, pipe, clickhouse · Missing: https docs, just released, exist

89%89% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, friendly · Missing: plus, intuitive, reviews

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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