metriql
csv-metabase-driver
Our great sponsors
metriql | csv-metabase-driver | |
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7 | 1 | |
284 | 177 | |
0.4% | - | |
1.9 | 0.0 | |
about 1 year ago | over 1 year ago | |
Kotlin | Clojure | |
Apache License 2.0 | - |
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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).
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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
csv-metabase-driver
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Open source Business intelligence platform made with Python
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...
What are some alternatives?
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications
datapane - Build and share data reports in 100% Python
evidence - Business intelligence as code: build fast, interactive data visualizations in pure SQL and markdown
steampipe - Zero-ETL, infinite possibilities. Live query APIs, code & more with SQL. No DB required.
perspective - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
mlcraft - Synmetrix – open source semantic layer / Boost your LLM precision
superset - Apache Superset is a Data Visualization and Data Exploration Platform
examples - Example apps and instrumentation for Honeycomb
Redash - Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
Multicorn - Data Access Library
Apache Calcite - Apache Calcite