stikkireddy

ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

great_expectations

Always know what to expect from your data.

delta

An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs

terraform-cdk

Define infrastructure resources using programming constructs and provision them using HashiCorp Terraform

scala-style-guide

Databricks Scala Coding Style Guide

delta-sharing

An open protocol for secure data sharing

terraform-provider-databricks

Databricks Terraform Provider

overwatch

Capture deep metrics on one or all assets within a Databricks workspace

slrp

rotating open proxy multiplexer

dataframe-rules-engine

Extensible Rules Engine for custom Dataframe / Dataset validation

mlflow-export-import

Export and import MLflow experiments, runs or registered models

databricks-sync

An experimental tool to synchronize source Databricks deployment with a target Databricks deployment.

dlt-with-debug

A lightweight helper utility which allows developers to do interactive pipeline development by having a unified source code for both DLT run and Non-DLT interactive notebook run.

deltaray

Delta reader for the Ray open-source toolkit for building ML applications

terraform-module-azure-datalake

Terraform module for an Azure Data Lake

metabase-sparksql-databricks-driver

kdbspark

Spark Data Source (V2) for Kx Systems kdb+ Database

edw-best-practices

Git Repo for EDW Best Practice Assets on the Lakehouse

transformer_runner

ray-search

datafussion-delta-rust

Query Delta without SPARK

echo-server