Alternatives
Products that do what PostgresML, end-to-end machine learning in your favorite db does
- 1PN
We've been hard at work for a few weeks and thought it's time for another update. In case you missed our first post, PostgresML is an end-to-end machine learning solution, running alongside your favorite database. This time we have more of a suite offering: project management, visibility into the datasets and the deployment pipeline decision making. Let us know what you think! Demo link is on the page, and also here: https://demo.postgresml.org
2022 · postgresml.org
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- 3IO
2024 · github.com
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- 7PI
This extension let's you write PRQL functions in PostgreSQL. When I first saw PRQL on Hacker News a few months ago, I was immediately captivated by the idea, yet equally disappointed that there was no integration for PostgreSQL. Having previous experience with writing PostgreSQL extensions in C, I thought this was a great opportunity to try out the pgrx framework and decided to integrate PRQL with PostgreSQL myself. The maintainers of both PRQL and pgrx were very nice to work with. Thanks guys.
2024 · github.com
- 8PT
PgQueuer is a minimalist, high-performance job queue library for Python, leveraging the robustness of PostgreSQL. Designed for simplicity and efficiency, PgQueuer uses PostgreSQL's LISTEN/NOTIFY to manage job queues effortlessly.
2024 · github.com
- 9PA
2024 · github.com
- 10PE
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too. The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our…
Mar 2026 · github.com
- 11H1
hi hn, hydra ceo here hydra is an open source, column-oriented postgres. you can set up remarkably fast aggregates on your project in minutes to query billions of rows instantly. postgres is great, but aggregates can take minutes to hours to return results on large data sets. long-running analytical queries hog database resources and degrade performance. use hydra to run much faster analytics on postgres without making code changes. data is automatically loaded into columnar format and compressed. connect to hydra with your preferred postgres client (psql, dbeaver, etc). following 4 months…
2023 · hydra-so.notion.site
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- 13DL
2014 · databaselabs.io
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- 16GF
2015 · github.com
- 17MO
Hi HN! We just released the public alpha version of Mathesar (https://mathesar.org/, code: https://github.com/centerofci/mathesar). Mathesar is an open source tool that provides a spreadsheet-like interface to a PostgreSQL database. I was originally inspired by wanting to build something like Dabble DB. I was in awe of their user experience for working with relational data. There’s plenty of “relational spreadsheet” software out there, but I haven’t been able to find anything with a comparable UX since Twitter shut Dabble DB down. We're a non-profit…
2023 · github.com
- 18PA
- 19DG
2019 · github.com
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Hi HN! I’m Eoin, founder of Sourcetable (https://sourcetable.com). Sourcetable is an AI-native spreadsheet that syncs with all your data. Users pair with an AI copilot that helps them do their spreadsheet work, as well as more database-centric analysis and SQL. Soucetable syncs with databases including Postgres, MySQL, and MongoDB, and over 100+ business applications including Stripe, Zendesk, Hubspot, Quickbooks and Google Analytics. That data is available in a spreadsheet, and any models you build automatically update in near-real-time as new data flows in. The core primitives…
2024
- 22PN
2020 · ongres.com
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- 24BP
Hi HN! We're Evgeny and Arjun, and we’re building a better way to do analytics with Postgres. We love Postgres for its simplicity, power, and rich ecosystem. But engineers have to still get bogged down with heavyweight and expensive OLAP systems when connecting an analytics data stack. Postgres is amazing at OLTP queries, but not for OLAP queries (large data scans and aggregations). Even in this case, we’ve still heard from countless scaling startups that they still try to use only a read replica to run analytics workloads since they don’t want to deal with the data engineering complexity of…
2024 · github.com
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