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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.

Prefect integrations for interacting with Great Expectations

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

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.

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.

📈 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.

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.

Source code for the gx cloud agent