Examples
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Updated
Aug 23, 2020 - Jupyter Notebook
Examples
State of Data Science Nevada Conference: Multi-track tutorial to create, provision, and version control AWS infrastructure to manage data pipelines effectively
Self learning project using IMDb datasets
Ensuring data quality in an e-commerce data set using Great Expectations.
Demo on Data Engineering using Great Expectations API
A pipeline to forecast the direction stock prices from data from eodhistoricaldata.com
Dockerizing Data Docs autogenerated by Great Expectations using FastAPI Jinja Templates .
Example projects using great expectations to validate data.
Using Great Expectations and Notion's API, this repo aims to provide data quality for our databases in Notion.
An ML pipeline to flip nfts that makes use of the cloud and containers.
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.
R&D around Data Engineering Tools
A collection of Databricks notebooks for testing and learning
A lightweight tool to get an AI Infrastructure Stack up in minutes not days. K3ai will take care of setup K8s for You, deploy the AI tool of your choice and even run your code on it.
Learn how to add data validation and documentation to a data pipeline built with dbt and Airflow.
Tutorial using Great Expectations library, validating and profiling data on a local PostgreSQL database.
Kafka-Spark jobs orchestrated with Airflow
Integrating Apache Airflow, dbt, Great Expectations and Apache Superset to develop a modern open source data stack.
Tutorial for implementing data validation in data science pipelines
A project for exploring how Great Expectations can be used to ensure data quality and validate batches within a data pipeline defined in Airflow.
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