Po

Pontoon, an open-source data export platform

Hacker News

Pontoon, an open-source data export platform

Hi HN, We’re Alex and Kalan, the creators of Pontoon ( https://github.com/pontoon-data/Pontoon ). Pontoon is an open-source data export platform that makes it really easy to create data syncs and send data to your enterprise customers. Check out our demo here: https://app.storylane.io/share/onova7c23ai6 or try it out with docker: https://pontoon-data.github.io/Pontoon/getting-started/quick... While at our prior roles as data engineers, we’ve both felt the pain of data APIs. We either had to spend weeks building out data pipelines in house or spend a lot on ETL tools like Fivetran ( https://www.fivetran.com/ ). However, there were a few companies that offered data syncs that would sync directly to our data warehouse (eg. Redshift, Snowflake, etc.), and when that was an option, we always chose it. This led us to wonder “Why don’t more companies offer data syncs?”. It turns out, building reliable cross-cloud data syncs is difficult. That’s why we built Pontoon. We designed Pontoon to be: - Easily deployed: we provide a single, self-contained Docker image for easy deployment and Docker Compose for larger workloads ( https://pontoon-data.github.io/Pontoon/getting-started/quick... ) - Support modern data warehouses: we support syncing to/from Snowflake, BigQuery, Redshift, and Postgres. - Sync cross cloud: sync from BigQuery to Redshift, Snowflake to BigQuery, Postgres to Redshift, etc. - Developer friendly: data syncs can also be built via the API - Open source: Pontoon is free to use by anyone Under the hood, we use Apache Arrow ( https://arrow.apache.org/ ) to move data between sources and destinations. Arrow is very performant - we wanted to use a library that could handle the scale of moving millions of records per minute. In the shorter-term, there are several improvements we want to make, like: - Adding support for DBT models to make adding data models easier - UX improvements like better error messaging and monitoring of data syncs - More sources and destinations (S3, GCS, Databricks, etc.) - Improve the API for a more developer friendly experience (it’s currently tied pretty closely to the front end) In the longer-term, we want to make data sharing as easy as possible. As data engineers, we sometimes felt like second class citizens with how we were told to get the data we needed - “just loop through this api 1000 times”, “you probably won’t get rate limited” (we did), “we can schedule an email to send you a csv every day”. We want to change how modern data sharing is done and make it simple for everyone. Give it a try: https://github.com/pontoon-data/Pontoon . Cheers!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
92%92% 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, dock, models · Missing: mac, agents, macos
89%89% 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
72%72% 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: way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, friendly · Missing: plus, intuitive, reviews
41%41% 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
14%14% 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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