metriql
datapane
metriql | datapane | |
---|---|---|
7 | 30 | |
284 | 1,348 | |
0.0% | 0.4% | |
1.9 | 7.3 | |
about 1 year ago | 7 months ago | |
Kotlin | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
metriql
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Getting started with a metrics store
Some of the companies that operate in space are Cube Dev; Transform(currently acquired by dbt); metriql. See more companies at https://www.moderndatastack.xyz/companies/metrics-store.
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Launch HN: Hydra (YC W22) – Query Any Database via Postgres
Presto is pretty successful but its focus is to be distributed query engine, not a proxy layer for the existing query engines. We use Trino ( formerly Presto) as our query layer and do something similar to Hydra at Metriql [1] with a fairly different use-case. Data people provide a semantic layer with the mecrics and expose them to 18+ downstream tools.
[1]: https://metriql.com
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How do you separate ML from analytics in your data pipeline?
This is why metrics store tooling have started appearing recently (e.g. TransformData, SuperGrain, Metriql, dbt Metrics) - to solve the problem of this table / metric disorganization across an org's data landscape.
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Open source Business intelligence platform made with Python
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
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Show HN: Low-Code Metrics Store
As a current Looker power-user, this looks really solid.
One thing I’m not sure about though: can you use the metrics outside of the native tool, and if so how?
That is, I see Looker as a BI tool, not a metrics layer, since you mainly use the metrics you define inside Looker, not in other tools. On the other hand, something like MetriQL[0] is a pure metrics layer that can supposedly be used anywhere.
Is this both? If so, some better documentation around how to use the metrics layer would be helpful (or maybe I just didn’t look in the right place).
[0] https://metriql.com/
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Notes on the Perfidy of Dashboards
3. Define metrics in one place on top of your data models and expose the metrics to all the data tools. (This layer is new, and we're tapping it at https://metriql.com)
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Launch HN: Evidence (YC S21) – Web framework for data analysts
We use BSL license and metriql is free with a single database target. If you want to connect multiple dbt projects in a single deployment, you need to go through the sales cycle.
We work with ETL vendors that use metriql to make revenue with our BI tool integrations so we picked BSL license to be able to structure our business model in a way that you should be required to pay only if you're reselling metriql to your customers.
You can find the license here: https://github.com/metriql/metriql
datapane
- Datapane: Build and share data reports in 100% Python
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Polars: Company Formation Announcement
If you're looking for an easy way to build an HTML report using Python, you might find Datapane (https://github.com/datapane/datapane) helpful. I'm one of the people building it! We don't support polars (yet, on the roadmap) but we do support pandas so you can convert to a pandas DataFrame and include your data and any plots, etc.
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JupyterLab 4.0
If you're interested in an easier way to create reports using Python and Plotly/Pandas, you should check out our open-source library, Datapane: https://github.com/datapane/datapane - you can create a standalone, redistributable HTML file in a few lines of Python.
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Evidence – Business Intelligence as Code
You might be interested in what we're hacking on at Datapane (I'm one of the founders): https://github.com/datapane/datapane.
You can create standalone HTML data reports from Python/Jupyter in ~3 lines of code: https://docs.datapane.com/reports/overview/
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
You can build web apps from Jupyter using Datapane [0]. I'm one of the founders, so let me know if I can help at all.
You can either export a static site [1] (and host on GH pages or S3), or, if you need backend logic, you can add Python functions [2] and serve on your favourite host (we use Fly).
We have specific Jupyter integration to automatically convert your notebook into an app [3].
[0] https://github.com/datapane/datapane
[1] https://docs.datapane.com/reference/reports/#datapane.proces...
[2] https://docs.datapane.com/apps/overview/
[3] https://docs.datapane.com/reports/jupyter-integration/#conve...
- Datapane – Build full-stack data apps in 100% Python
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Datapane - Build full-stack data apps in 100% Python
Our GitHub is https://github.com/datapane/datapane and you can get started here: https://docs.datapane.com/quickstart/
- Datapane: Build internal analytics products in minutes using Python
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Datapane - Build internal data products in 100% Python
Thanks a lot! Yes, absolutely, a few people have brought this up and working working on removing the header right now. If I can help at all, feel free to reach us on GH Discussions: https://github.com/datapane/datapane/discussions
- Datapane/datapane: Build full-stack data analytics apps in Python
What are some alternatives?
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications
streamlit - Streamlit — A faster way to build and share data apps.
evidence - Business intelligence as code: build fast, interactive data visualizations in pure SQL and markdown
dash - Data Apps & Dashboards for Python. No JavaScript Required.
steampipe - Zero-ETL, infinite possibilities. Live query APIs, code & more with SQL. No DB required.
jupyter-dash - OBSOLETE - Dash v2.11+ has Jupyter support built in!
mlcraft - Synmetrix – open source semantic layer / Boost your LLM precision
perspective - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
examples - Example apps and instrumentation for Honeycomb
superset - Apache Superset is a Data Visualization and Data Exploration Platform
csv-metabase-driver - A CSV metabase driver
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!