Marple – Interactive time series visualization for engineers

Hacker News

Hi HN, excited to share the project I have been working on for the past two years. Marple was created by engineers for engineers. Marple helps them organize and analyze their sensor data in a more user-friendly way. We’ve all been there: measurement data from an experiment or test needs to be analyzed but the only thing you have is a few data files flying around. You do not have a clue how to open or read the data. So what you do next is open your Python/Matlab/Jupyter Notebook/… and start coding in order to make some sense of the data. We didn’t like that process, so we started Marple to solve this issue. Engineers tend to log data at frequencies from 1Hz to 10kHz and usually log hundreds of sensors at the same time. Data sets usually contain millions of data points. In order to make our web-based data visualization responsive, we had some technical challenges to tackle. We developed our own visualization engine based on PostgreSQL that is able to visualize millions of data points pretty much instantly. This allows us to create an interactive visualization environment which is perfect for data exploration, even for large data sets! This is the second time we show Marple to HackerNews, but since then we made some big steps. We made a pivot to a cloud product and now offer a free version of Marple. Feel free to head over to our website and give it a go. Let us know what you think of it!

Visit product View on Hacker NewsJul 26, 2022

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

11%11% 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.

Correct prediction on native model

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