Census – Export for data warehouses

Hacker News

Hello Hacker News! We're a team of YC founders (Meldium W13, Draft S11, TapEngage S11) launching something new ( https://www.getcensus.com ). How many times has your business team asked you to generate yet another CSV file, write a ”quick report” in SQL, or send some custom data to a terrible API (looking at you Marketo)? We’ve built a product that connects directly to your data warehouse and syncs into apps like Salesforce, Customer.io and even Google Sheets. In fact, your business teams won’t even need to rely on engineering to manage all these pipelines. The tech stack for analyzing customer data in 2020 looks pretty great. You can load almost any data into an auto-scaling data warehouse (Snowflake, BigQuery) with easy point & click tools like Fivetran. You can build SQL models with dbt and create visual reports in Metabase. But you can’t easily push insights back into the marketing/sales/support apps. You can’t solve this with direct app integrations or “event routers” like Zapier and you definitely shouldn’t over-engineer a solution with Spark/Kafka/Airflow. We designed Census to make your data warehouse a single source of truth for modeling and transformation before publishing data back into your SaaS tools quickly and reliably. We’re proud of what we’ve built so far and there’s a lot more work to deliver on our dream of saving us all from generating & uploading yet another CSV file so we can spend more time actually building our products (or reading HN). You can check it out at https://www.getcensus.com . Since this is HN, we’d love to hear everyone’s war stories on building internal ETL solutions!

Visit product View on Hacker NewsApr 30, 2020

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, apps · Missing: mac, agents, macos

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

64%64% 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: apps, google · Missing: mobile apps, ios, personal

40%40% 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

23%23% 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 · 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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