| Name | Version | Summary | date |
| mu-pipelines-execute-spark |
0.3.0 |
mu_pipelines_execute_spark |
2025-02-25 04:13:50 |
| FabricSync |
2.1.5 |
Fabric BigQuery Data Sync Utility |
2025-02-19 16:13:04 |
| lakehouse-ns |
0.4.0 |
A library with the Lakehouse Framework |
2025-02-19 07:24:00 |
| aistore |
1.12.2 |
Client-side APIs to access and utilize clusters, buckets, and objects on AIStore. |
2025-02-19 01:21:04 |
| sling-windows-amd64 |
1.4.3.post1 |
Sling Binary for Windows |
2025-02-17 16:55:11 |
| sling-mac-arm64 |
1.4.3.post1 |
Sling Binary for Mac (ARM64) |
2025-02-17 16:53:38 |
| mu-pipelines-driver |
0.3.1 |
mu_pipelines_driver |
2025-02-16 20:35:45 |
| mu-pipelines-interfaces |
0.1.5 |
mu_pipelines_interfaces |
2025-02-16 19:35:22 |
| rushdb |
0.3.0 |
RushDB Python SDK |
2025-02-14 18:33:39 |
| pg-upsert |
1.5.3 |
A Python library for upserting data into postgres. |
2025-02-12 16:46:34 |
| SQLDataModel |
1.3.0 |
SQLDataModel is a lightweight dataframe library designed for efficient data extraction, transformation, and loading (ETL) across various sources and destinations, providing an efficient alternative to common setups like pandas, numpy, and sqlalchemy while also providing additional features without the overhead of external dependencies. |
2025-02-09 21:30:51 |
| mu-pipelines-destination-spark |
0.1.2 |
mu_pipelines_destination_spark |
2025-02-08 22:28:54 |
| sling-linux-amd64 |
1.4.2 |
Sling Binary for Linux (AMD64) |
2025-02-03 21:43:38 |
| sling-mac-amd64 |
1.4.1.post3 |
Sling Binary for Mac (AMD64) |
2025-02-03 20:08:43 |
| sling-linux-arm64 |
1.4.1.post3 |
Sling Binary for Linux (ARM64) |
2025-02-03 20:07:39 |
| rivusio |
0.2.0 |
A type-safe, async-first data processing pipeline framework |
2025-02-02 06:57:24 |
| yato-lib |
0.0.17 |
The smallest DuckDB SQL transformations orchestrator |
2025-02-01 16:27:08 |
| dbt_coves |
1.8.15 |
CLI tool for dbt users adopting analytics engineering best practices. |
2025-01-22 20:34:16 |
| etl-utilities |
0.10.10 |
This repository provides a collection of utility functions and classes for data cleaning, SQL query generation, and data analysis. The code is written in Python and uses libraries such as `pandas`, `numpy`, and `dateutil`. |
2025-01-16 14:37:28 |
| logprep |
15.1.1 |
Logprep allows to collect, process and forward log messages from various data sources. |
2025-01-13 13:04:33 |