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Ascend.io – Smarter Data Pipelines

Hacker News

Ascend.io – Smarter Data Pipelines

Hi HN, I’m Sean, the founder of Ascend.io (https://www.ascend.io). I’m really excited to post here and announce the launch of Ascend.io, a radical new way of designing, scaling, and automating data pipelines. Ascend is the result of nearly 4 years of development effort for a team that is now 30-strong, and I would love for you to give it a test drive and let me what you think. I’ve felt this pain since I wrote my first MapReduce in 2004 (using Sawzall @ Google), and in the 15 years since, things have not improved at the pace of other parts of the technology ecosystem. When I went about trying to fix this, I took a page from other technologies, particularly the migration to declarative models such as those found in React and Kubernetes. With Ascend, you just declare what you want the end-state of your data to be and the Dataflow Control Plane automates the orchestration, infrastructure, and persistence to make it happen. Data can arrive late, schemas can shift, logic can be updated, and the control plane generates new pipelines to propagate, backfill, and repair existing datasets. With this approach, pipelines take a lot less code to build (current record is reducing LOC from 100k to 2k), they break less, and you get to actually sleep through the night without being paged. I certainly think this is a huge step forward for the world of pipeline development and making pipelines suck less. But, I really want to know what you all think! Anyone who is interested, I can get you setup with a free trial (https://www.ascend.io/get-started). Can’t wait to hear the feedback! ~Sean

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, new · Missing: mac, agents, macos
84%84% 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.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
75%75% 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: started · Missing: supports, reddit linkedin, podcasting
63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · 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 · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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: smart · 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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