diepvries
ibis
| diepvries | ibis | |
|---|---|---|
| 3 | 23 | |
| 124 | 4,271 | |
| 2.4% | 7.1% | |
| 5.4 | 10.0 | |
| 20 days ago | 6 days ago | |
| Python | Python | |
| MIT License | 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.
diepvries
-
To those who have implemented a Data Vault (2.0) model: Would you do it again?
Regarding library support, totally agreed. We even open-sourced our own library for generating SQL statements. We currently only support Snowflake, but would like to support more backends in the future. If you're on dbt, there's always dbtvault.
- diepvries, a Python library to generate Data Vault SQL statements
- Diepvries – a Python library to generate Data Vault SQL statements
ibis
-
Show HN: Hashquery, a Python library for defining reusable analysis
I really don't understand the appeal of dbt vs a proper programming language. The templating approach leads to massive spaghetti. I look forward to trying out something like Ibis [0]
0: https://ibis-project.org/
-
This Week In Python
ibis – portable Python dataframe library
- Ibis: The portable Python dataframe library
- FLaNK Stack 26 February 2024
-
Quarto
The main benefit is that you get a Python (or R, Julia or Rust) interpreter. So you can evaluate code. A good example of the value of this is the Ibis docs which use Quarto: https://ibis-project.org/
-
Polars – A bird's eye view of Polars
Ive found polars quite intuitive, though for python, I lean more towards [ibis](https://ibis-project.org/). The interface is nearly identical, but ibis has the benefit if building sql queries before pulling any actual data (like dbplyr) — whereas polars requires the data to be in-memory (at least for rdb’s, though correct me if Im wrong)
this to me seems like a good argument for only using ibis, but Im happy to be convinced otherwise
- Ibis – Universal Interface for Data Wrangling
-
Vanna.ai: Chat with your SQL database
Please add Ibis Birdbrain https://ibis-project.github.io/ibis-birdbrain/ to the list. Birdbrain is an AI-powered data bot, built on Ibis and Marvin, supporting more than 18 database backends.
See https://github.com/ibis-project/ibis and https://ibis-project.org for more details.
- Ibis
What are some alternatives?
CueObserve - Timeseries Anomaly detection and Root Cause Analysis on data in SQL data warehouses and databases
snowflake-connector-python - Snowflake Connector for Python
jupysql - Better SQL in Jupyter. 📊
PySpark-Boilerplate - A boilerplate for writing PySpark Jobs
dbd - dbd is a database prototyping tool that enables data analysts and engineers to quickly load and transform data in SQL databases.
Apache Impala - Apache Impala
pangres - SQL upsert using pandas DataFrames for PostgreSQL, SQlite and MySQL with extra features
sqlite_scanner - DuckDB extension to read and write to SQLite databases
katacoda
nodejs-polars - nodejs front-end of polars
django-clickhouse - This project's goal is to build Yandex ClickHouse database into Django project.
store - PostgreSQL shopping cart