#Etl-framework
Showing 14 of 14 repositories tagged #etl-framework, ranked by stars
Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
Data pipelines for cloud config and security data. Build cloud asset inventory, CSPM, FinOps, and vulnerability management solutions. Extract from AWS, Azure, GCP, and 70+ cloud and SaaS sources.
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
An end-to-end GoodReads Data Pipeline for Building Data Lake, Data Warehouse and Analytics Platform.
This repository is a getting started guide to Singer.
A scalable general purpose micro-framework for defining dataflows. THIS REPOSITORY HAS BEEN MOVED TO www.github.com/dagworks-inc/hamilton
A Task-Based Workflow Orchestration Engine
A tool for building feature stores.
A modern data marketplace that makes collaboration among diverse users (like business, analysts and engineers) easier, increasing efficiency and agility in data projects on AWS.
Structured Data Extractor for AI Agents. Search your documents or the web for specific data and get it back in JSON or Markdown in a single tool call.
Data pipelines from re-usable components
Extract Load Transform (ELT) framework is a metadata based batch orchestration framework for modern data platforms. Implemented using Azure PaaS data services. Common ingestion and transformation patterns available out of box. Reusable code can be easily extended to cater to custom patterns.
Stellar ETL will enable real-time analytics on the Stellar network
Building a next-generation hybrid data pipeline architecture that combines the power of Microsoft Fabric, Azure Cloud, and Power BI. This pipeline is engineered to tackle the challenges of real-time data ingestion, multi-layered processing, and analytics, delivering business-critical insights.