jupysql
dbt-ml-preprocessing
| jupysql | dbt-ml-preprocessing | |
|---|---|---|
| 8 | 2 | |
| 605 | 175 | |
| 4.6% | 0.0% | |
| 9.1 | 3.7 | |
| 21 days ago | 10 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
jupysql
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Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor)
Hey, HN community!
We're stoked to launch JupySQL today! JupySQL is an open-source library that brings a modern SQL experience to Jupyter. JupySQL is compatible with all major databases, such as Snowflake, Redshift, PostgreSQL, MySQL, MariaDB, DuckDB, SQL Server, Clickhouse, Trino, and more!
To get started, check out our tutorial: https://jupysql.ploomber.io/en/latest/quick-start.html
SQL is the defacto language for data analysis; however, analysis often requires a mix of SQL and Python. JupySQL bridges this gap, allowing users to execute SQL queries seamlessly in Jupyter and continue their analysis in Python. Add %%sql to the top of your cell and start writing SQL.
Here are some of JupySQL's main features:
- Syntax highlighting
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JupySQL: Connecting to a SQL database from Jupyter
Please show your support with a 🌟: https://github.com/ploomber/jupysql
- GitHub - ploomber/jupysql: Better SQL in Jupyter. 📊
- SQL CTE's in Jupyter notebooks, DuckDB integration and more
- TL;DR incorporate SQL functionality within Jupyter, access to modern data processing DBs (like DuckDB), polars and data exploration through plotting easier with JupySQL.
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Evidence – Business Intelligence as Code
If anyone is looking for something like this in Python/Jupyter, check out JupySQL: https://github.com/ploomber/jupysql
- A full-featured SQL client for Jupyter
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Pandas v2.0 Released
How are people managing the existence of data frame APIs like pandas/polars with SQL engines like BigQuery, Snowflake, and DuckDB?
Most of my notebooks are a mix of SQL and Python: SQL for most processing, dump the results as a pandas dataframe (via https://github.com/ploomber/jupysql) and then use Python for operations that are difficult to express with SQL (or that I don't know how to do it), so I end up with 80% SQL, 20% Python.
Unsure if this is the best workflow but it's the most efficient one I've come up with.
Disclaimer: my team develops JupySQL.
dbt-ml-preprocessing
What are some alternatives?
grai-core
CueObserve - Timeseries Anomaly detection and Root Cause Analysis on data in SQL data warehouses and databases
tpch
airflow-dbt - Apache Airflow integration for dbt
datapane - Build and share data reports in 100% Python
dbt-snowflake-monitoring - A dbt package from SELECT to help you monitor Snowflake performance and costs
nba-monte-carlo - Monte Carlo simulation of the NBA season, leveraging dbt, duckdb and evidence.dev
datavault4dbt - Scalefree's dbt package for a Data Vault 2.0 implementation congruent to the original Data Vault 2.0 definition by Dan Linstedt including the Staging Area, DV2.0 main entities, PITs and Snapshot Tables.
chdb-server-bak - API Server for chDB, an in-process SQL OLAP Engine powered by ClickHouse
sqlglot - Python SQL Parser and Transpiler
pytest-mock-resources - Pytest Fixtures that let you actually test against external resource (Postgres, Mongo, Redshift...) dependent code.
dbt-coves - CLI tool for dbt users to simplify creation of staging models (yml and sql) files