Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data. It does that today by indexing data resources (tables, dashboards, streams, etc.) and powering a page-rank style search based on usage patterns (e.g. highly queried tables show up earlier than less queried tables). Think of it as Google search for data. The project is named after Norwegian explorer Roald Amundsen, the first person to discover South Pole.
It includes three microservices and a data ingestion library.
amundsenfrontendlibrary: Frontend service which is a Flask application with a React frontend.
amundsensearchlibrary: Search service, which leverages Elasticsearch for search capabilities, is used to power frontend metadata searching.
amundsenmetadatalibrary: Metadata service, which leverages Neo4j or Apache Atlas as the persistent layer, to provide various metadata.
amundsendatabuilder: Data ingestion library for building metadata graph and search index. Users could either load the data with a python script with the library or with an Airflow DAG importing the library.
It includes three microservices and a data ingestion library.
amundsenfrontendlibrary: Frontend service which is a Flask application with a React frontend. amundsensearchlibrary: Search service, which leverages Elasticsearch for search capabilities, is used to power frontend metadata searching. amundsenmetadatalibrary: Metadata service, which leverages Neo4j or Apache Atlas as the persistent layer, to provide various metadata. amundsendatabuilder: Data ingestion library for building metadata graph and search index. Users could either load the data with a python script with the library or with an Airflow DAG importing the library.