Kestra - Open-Source Airflow Alternative

Hacker News

Hey HN, I'm really proud to share with you my new open source project: Kestra https://github.com/kestra-io/kestra I created a few years ago a successful open source AKHQ project: https://github.com/tchiotludo/akhq (renamed from KafkaHQ) which has been adopted by big companies like Best Buy, Pipedrive, BMW, Decathlon and many more. 2300 stars, 120 contributors, 10M docker downloads, much more than I expected. Now let's talk about Kestra, an infinitely scalable orchestration and scheduling platform for creating, running, scheduling and monitoring millions of complex pipelines. I started the project 30 months ago and I'm even more proud of this project that required a lot of investment and time to build the future of data pipelines (I hope). The result is now ready to be presented and I hope to get some feedback from you, HN community. To have a fully scalable solution, we choose Kafka as our database (of course, I love Kafka if you didn't know) as well as ElasticSearch, Micronaut, ... and can be deployed on Kubernetes, VM or on premise. You may think there are many alternatives in this area, but we decided to take a different road by using a descriptive approach (low code) to build your pipelines allowing to edit directly from the web interface and deploy to production with terraform directly. We paid a lot of attention to the scalability and performance part which allows us to have already a big production at a big French retailer: Leroy Merlin Since Kestra core is plugin based, many are available from the core team, but you can create one easily. More information: - on the official website: https://kestra.io/ - on the medium post: https://medium.com/@kestra-io/introducing-kestra-infinitely-... - check out the project: https://github.com/kestra-io/kestra Your comments are more than welcome, thank you!

Visit product View on Hacker NewsMar 24, 2022

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

87%87% 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: dock, new, using · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released

80%80% 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 · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

42%42% 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 · Strong signals: paid · 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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