Open source Business intelligence platform made with Python

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • superset

    Apache Superset is a Data Visualization and Data Exploration Platform

  • cube.js

    📊 Cube — Open-Source Analytics API for Building Data Apps

    Was about to say the same. Last year I went through a huge industry comparison exercise for my former company. Spent 6 months evaluating BI tooling. Every option out there is ridiculously expensive. Looker quoted us at $1M/year, though they were using the sticker shock strategy to get us around a $200K/year price point.

    I came away with the impression that BI is simply not accessible outside of enterprise level orgs. So I love seeing tools like this come into the market. You should also check out https://cube.dev - they're much less out of the box, but super flexible for developers.

  • OPS

    OPS - Build and Run Open Source Unikernels. Quickly and easily build and deploy open source unikernels in tens of seconds. Deploy in any language to any cloud.

  • datapane

    Datapane is the easiest way to create data science reports from Python.

    Can you do the analysis/visualization in Python? If so, you could use https://github.com/datapane/datapane to build and share a report or dashboard (either as an HTML file, or publish it for free on datapane.com). I'm one of the people building it, so let me know if I can help!

  • metriql

    The metrics layer for your data. Join us at https://metriql.com/slack

    We're using Superset to enable our analysts to explore our clients' SEM/SEO/analytics data. It also posts alerts to Slack when, say, the daily session count of a website isn't what was expected given the historical data.

    Yeah, it's a little rough to get going, but once it is, we've found it to be a really powerful (and actively developed!) BI tool. It's even better with dbt + MetriQL [0], which can automatically sync Superset's dataset metadata directly with properties you set up in dbt.

    Adding custom visualizations is much harder than it should be, but they're very much aware of that, and working to address it. Their Slack community is super-helpful, too.

    [0]: https://metriql.com

  • Redash

    Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.

    I remember doing the research a few years ago. Redash was the final choice because it supports mongodb.

    https://github.com/getredash/redash

  • csv-metabase-driver

    A CSV metabase driver

    there is a csv driver[1] which allows you map a csv file on the metabase server, or providing a url of the csv. But I guess most people (including myself) would think of "upload csv file via UI" when it comes to csv support.

    [1] https://github.com/Markenson/csv-metabase-driver/releases/ta...

  • perspective

    A data visualization and analytics component, especially well-suited for large and/or streaming datasets.

  • Scout APM

    Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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