Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
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Updated
May 25, 2024 - Python
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
An orchestration platform for the development, production, and observation of data assets.
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
🧙 Build, run, and manage data pipelines for integrating and transforming data.
Use this template repository to write projects and tenders data ingestion pipelines
Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.
Relational data pipelines for the science lab
dbt package that is part of Elementary, the dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
Cloud-native, data onboarding architecture for Google Cloud Datasets
Developed a data pipeline to automate data warehouse ETL by building custom airflow operators that handle the extraction, transformation, validation and loading of data from S3 -> Redshift -> S3
One framework to develop, deploy and operate data workflows with Python and SQL.
Conductor OSS SDK for Python programming language
Work with your web service, database, and streaming schemas in a single format.
The practical use-cases of how to make your Machine Learning Pipelines robust and reliable using Apache Airflow.
ARAKAT - Big Data Analysis and Business Intelligence Application Development Platform
Example of an ETL Pipeline using Airflow
Building data processing pipelines for documents processing with NLP using Apache NiFi and related services
Source code for guide to run Apache Airflow on Kubernetes
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Introduction to Data Pipelines and Apache Airflow
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