Multicorn
gooddata-python-sdk
| Multicorn | gooddata-python-sdk | |
|---|---|---|
| 8 | 2 | |
| 694 | 25 | |
| 0.4% | - | |
| 0.0 | 9.6 | |
| over 1 year ago | 3 days ago | |
| Python | Python | |
| PostgreSQL License | GNU General Public License v3.0 or later |
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.
Multicorn
- Framework to get email attachments into Data Warehouse
- Multicorn – PostgreSQL extension to make Foreign Data Wrapper development easy
- Supabase Wrappers: A Framework for Building Postgres Foreign Data Wrappers
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Headless BI: Metric Standardization in Action
GoodData Foreign Data Wrapper is a PostgreSQL foreign data wrapper extension. It is built on top of multicore, and it makes GoodData.CN’s metrics, calculations, and data available in PostgreSQL as tables.
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Launch HN: Hydra (YC W22) – Query Any Database via Postgres
This is really nice! Congrats!
I once started building as a side project something similar but focused on querying cloud resources (like S3 buckets, ec2s, etc... discovering the biggest file from a bucket was trivial with this). I abandoned the project but someone else built a startup on the same concept - even the name was the same: cloudquery.
I built it using the multicorn [1] postgres extension and it is deligthful of how easy it to get something simple running.
[1] https://multicorn.org/
- Creating a Postgres Foreign Data Wrapper
gooddata-python-sdk
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Why You Should Use Python For Your Next Project
Our product contains a Headless BI engine where you can connect any application, data platform, or visualization tool to the engine’s semantic layer and consume the same consistent analytics anywhere. Connecting to a semantic layer with the help of GoodData Python libraries is very straightforward! GoodData Pandas allows creating pandas series and data frames from the computations done against a semantic model in GoodData.CN. As mentioned above, imagine you have a database with many tables, and you want to get a data frame consisting of columns from various joined tables. Usually, you have to do many joins manually in SQL before getting the desired data frame in Pandas. But if you connect the database to GoodData.CN, you can forget joins — with the help of a semantic model and GoodData Pandas, you will get your desired data frame with much less hassle. Just for demonstration, compare the two following code snippets. The first one is just pure pandas:
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Headless BI: Metric Standardization in Action
To follow this article, you can download GoodData Python SDK, which contains a docker-compose file, and run the following command in the root folder:
What are some alternatives?
dbeaver - Free universal database tool and SQL client
steampipe - Zero-ETL, infinite possibilities. Live query APIs, code & more with SQL. No DB required.
gooddata-ui-sdk - GoodData.UI SDK
multicorn2
NumPy - The fundamental package for scientific computing with Python.
pgx - Build Postgres Extensions with Rust! [Moved to: https://github.com/tcdi/pgrx]
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
metriql - The metrics layer for your data. Join us at https://metriql.com/slack
examples - TensorFlow examples
wundergraph - WunderGraph is a Backend for Frontend Framework to optimize frontend, fullstack and backend developer workflows through API Composition.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production