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* Fix explain turned by default, use dfs_query_then_fetch in cases of sharding of search cluster * Add exact match configs * Add exact match configs * Update Logic to build search source builder with exact match priority * Revert "Update Logic to build search source builder with exact match priority" This reverts commit 175a2e9c6b67ee90d4b2a35af89bb035e8c45131. * Revert "Add exact match configs" This reverts commit 3fd52606610bbb97a676170004cab6d7adc31a0d. * revert display name change * make boost mode as sum by defaul * add more fqnparts for schema and database * revert DFS_QUERY_THEN_FETCH since sharding wasn the issue * use fqn split * refactor fqn parsing --------- Co-authored-by: mohitdeuex <mohit.y@deuexsolutions.com>
This guide will help you setup the Ingestion framework and connectors
OpenMetadata Ingestion is a simple framework to build connectors and ingest metadata of various systems through OpenMetadata APIs. It could be used in an orchestration framework(e.g. Apache Airflow) to ingest metadata. Prerequisites
- Python >= 3.8.x
Docs
Please refer to the documentation here https://docs.open-metadata.org/connectors
TopologyRunner
All the Ingestion Workflows run through the TopologyRunner.
The flow is depicted in the images below.
TopologyRunner Standard Flow
TopologyRunner Multithread Flow
