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Due to apply_default decorator removal, this version of the provider requires Airflow 2.1.0+. If your Airflow version is 2.1.0, and you want to install this provider version, first upgrade Airflow to at least version 2.1.0. Otherwise, your Airflow package version will be upgraded automatically, and you will have to manually run airflow upgrade db to complete the migration. This operator currently works with the Great Expectations V3 Batch Request API only. If you would like to use the...

The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from...

Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative...

SQLBucket is a lightweight framework to help write, orchestrate and validate SQL data pipelines. It gives the possibility to set variables and introduces some control flow using the fantastic Jinja2 library. It also implements a very simplistic unit and integration test framework where you can validate the results of your ETL in the form of SQL checks. With SQLBucket, you can apply TDD principles when writing data pipelines. To start working, you need to instantiate your SQLBucket core object...

.... Simple data manipulation jobs can be created visually. More complex business logic can be implemented using Clover's domain-specific-language CTL, in Java or languages like Python or JavaScript. Through its DataServices functionality, it allows to quickly turn data pipelines into REST API endpoints. The platform allows to easily scale your data job across multiple cores or nodes/machines. Supports Docker/Kubernetes deployments and offers AWS/Azure images in their respective marketplace