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Real Time Analytics SaaS

Hacker News

Real Time Analytics SaaS

We have been involved in a number of real time streaming projects using tools such as Flink, Spark Streams and Kafka Streams backed by "real time" databases such as Druid. We always found these projects quite complex to develop and run, with stream processing in particular being a bit of a dark art. A stream to stream join in Flink can get quite mind bending for instance. In 2020 we had the idea of building a low code SaaS product for real time streaming analytics. The first attempt failed due to being a little over-engineered and with too many changes of direction, but over the last few months we have been building this with a simpler architecture and leaning on Clickhouse more. The SaaS is now live and we have a handful of customers in production and seeing the value. I would love feedback on the idea from the HN community and suggestions where to take the product next from a community that are likely to understand the value proposition and be familiar with the alternatives. Splunk is the main one that we get compared to so far. Website - http://timeflow.systems Walkthrough - https://www.youtube.com/watch?v=x4-WEHPt5-E A few blogs for our perspective and motivations: Build Vs Buy - https://timeflow.systems/build-vs-buy-for-real-time-event-streaming-platforms Why Is Stream Processing Hard - https://timeflow.systems/challenges-of-stream-processing-apps Why Move To Real Time - https://timeflow.systems/how-moving-from-batch-to-real-time-improves-the-customer-experience Thanks! Ben

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, clickhouse, io · Missing: https docs, excited, just released
76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using, code · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
33%33% 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
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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