great-expectations

🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴

The Lakehouse Engine is a configuration driven Spark framework, written in Python, serving as a scalable and distributed engine for several lakehouse algorithms, data flows and utilities for Data Products.

Sample project to demonstrate data engineering best practices

Nyc_Taxi_Data_Pipeline - DE Project

Learn how to create reliable ML systems by testing code, data and models.

Data Quality Gate based on AWS

Tutorial for implementing data validation in data science pipelines

How to evaluate the Quality of your Data with Great Expectations and Spark.

This repository serves as a comprehensive guide to effective data modeling and robust data quality assurance using popular open-source tools

Prefect integrations for interacting with Great Expectations

📈 A scalable, production-ready data pipeline for real-time streaming & batch processing, integrating Kafka, Spark, Airflow, AWS, Kubernetes, and MLflow. Supports end-to-end data ingestion, transformation, storage, monitoring, and AI/ML serving with CI/CD automation using Terraform & GitHub Actions.

A Covid-19 data pipeline on AWS featuring PySpark/Glue, Docker, Great Expectations, Airflow, and Redshift, templated in CloudFormation and CDK, deployable via Github Actions.

A project for exploring how Great Expectations can be used to ensure data quality and validate batches within a data pipeline defined in Airflow.

ELT Data Pipeline implementation in Data Warehousing environment

BirdiDQ leverages the power of the Python Great Expectations open-source library and combines it with the simplicity of natural language queries to effortlessly identify and report data quality issues, all at the tip of your fingers.

Code to demonstrate data engineering metadata & logging best practices

Run greatexpectations.io on ANY SQL Engine using REST API. Supported by FastAPI, Pydantic and SQLAlchemy as best data quality tool

Integrating Apache Airflow, dbt, Great Expectations and Apache Superset to develop a modern open source data stack.

This library is inspired by the Great Expectations library. The library has made the various expectations found in Great Expectations available when using the inbuilt python unittest assertions.

Using Great Expectations and Notion's API, this repo aims to provide data quality for our databases in Notion.