Amundsen Databuilder is a data ingestion library, which is inspired by [Apache Gobblin](https://gobblin.apache.org/). It could be used in an orchestration framework(e.g. Apache Airflow) to build data from Amundsen. You could use the library either with an adhoc python script([example](https://github.com/lyft/amundsendatabuilder/blob/master/example/scripts/sample_data_loader.py)) or inside an Apache Airflow DAG([example](https://github.com/lyft/amundsendatabuilder/blob/master/example/dags/sample_dag.py)).
Amundsen Databuilder is a data ingestion library, which is inspired by [Apache Gobblin](https://gobblin.apache.org/). It could be used in an orchestration framework(e.g. Apache Airflow) to build data from Amundsen. You could use the library either with an adhoc python script([example](https://github.com/lyft/amundsendatabuilder/blob/master/example/scripts/sample_data_loader.py)) or inside an Apache Airflow DAG([example](https://github.com/lyft/amundsendatabuilder/blob/master/example/dags/hive_sample_dag.py)).
For information about Amundsen and our other services, visit the [main repository](https://github.com/lyft/amundsen#amundsen)`README.md` . Please also see our instructions for a [quick start](https://github.com/lyft/amundsen/blob/master/docs/installation.md#bootstrap-a-default-version-of-amundsen-using-docker) setup of Amundsen with dummy data, and an [overview of the architecture](https://github.com/lyft/amundsen/blob/master/docs/architecture.md#architecture).
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An extractor that takes list of dict from user through config.
An extractor that extracts last updated time from Hive metastore and underlying file system. Although, hive metastore as a parameter called "last_modified_time", but it cannot be used as it provides DDL timestamp not DML timestamp.
An extractor that extracts last updated time from Hive metastore and underlying file system. Although, hive metastore has a parameter called "last_modified_time", but it cannot be used as it provides DDL timestamp not DML timestamp.
For this reason, HiveTableLastUpdatedExtractor is utilizing underlying file of Hive to fetch latest updated date. However, it is not efficient to poke all files in Hive, and it only pokes underlying storage for non-partitioned table. For partitioned table, it will fetch partition created timestamp, and it's close enough for last updated timestamp.
As getting metadata from files could be time consuming there're several features to increase performance.
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@@ -507,4 +507,4 @@ Deletion is always scary and it's better to perform dryrun before put this into
