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Restapp.io – SQL Data Modeling Tool in No/Low Code

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Restapp.io – SQL Data Modeling Tool in No/Low Code

Hey all! We've been working on RestApp V1 and this is our first time posting it on HN. It's an No/Low Code data modeling tool that enables you to build & maintain data pipelines with a visual programming interface. We don't store your data but we compute them through Apache Spark for query speed & efficiency. Here's some features: `Connectors: Connect to any source and destinations (DB, DWH and SaaS Applications). We currently support MongoDB, Snowflake, BigQuery, MySQL, MSSQL, SFTP (JSON, txt, csv, excel files supported), Hubspot, Stripe, GDrive (JSON, txt, csv, excel files supported). `Pipeline: Visual Programming Interface where you drag-and-drop SQL, NoSQL & Python functions instead of writing them to create a query and debug it easily. `Automation: You can automate your data pipeline (Job) through a scheduler. `Domain: Think of it like a workspace in which you can share securely your connectors and pipelines to specific users (colleagues, partners, clients...) We've designed this because as a data team member, we were writing a lot of long SQL queries with bad performances and we were getting headaches by debugging them. Now you can build, monitor and debug any kind of data pipelines with just Drag-and-drop built-in SQL functions to save you tremendous amount of time & effort. We're working on this continuously so we're keen to hear any feedback. Feature requests and critique are more than welcome. Try it out for free (30min of computing time offered each month): https://os.restapp.co/signup The Getting Started docs are here for anyone who wants to check this out: https://documentator.io/d/documentation-restapp and https://restapp.io/blog/how-to-build-data-pipelines-in-no-co...

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, stripe · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, users · 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: interface, users · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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