description:Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
@@ -42,7 +42,7 @@ that can be used for data exploration and it is what we leverage internally at L

4. Next, We need to build a preview client following this [guide](../frontend/docs/examples/superset_preview_client.md)
and the [example client code](https://github.com/lyft/amundsenfrontendlibrary/blob/master/amundsen_application/base/examples/example_superset_preview_client.py).
and the [example client code](https://github.com/amundsen-io/amundsenfrontendlibrary/blob/master/amundsen_application/base/examples/example_superset_preview_client.py).
There are a couple of things to keep in mind:
- We could start with an unauthenticated Superset([example superset config](https://gist.github.com/feng-tao/b89e6faf7236372cef70a44f13615c39)),
but in production, we will need to send the impersonate info to Superset
@@ -10,9 +10,9 @@ The doc won't cover how to setup a postgres database.
1. In the example, we have a postgres table in localhost postgres named `films`.

2. We leverage the [postgres metadata extractor](https://github.com/lyft/amundsendatabuilder/blob/master/databuilder/extractor/postgres_metadata_extractor.py)
2. We leverage the [postgres metadata extractor](https://github.com/amundsen-io/amundsendatabuilder/blob/master/databuilder/extractor/postgres_metadata_extractor.py)
to extract the metadata information of the postgres database. We could call the metadata extractor
in an adhoc python function as this [example](https://github.com/lyft/amundsendatabuilder/pull/248/commits/f5064e58a19a5bfa380b333cfc657ebb34702a2c)
in an adhoc python function as this [example](https://github.com/amundsen-io/amundsendatabuilder/pull/248/commits/f5064e58a19a5bfa380b333cfc657ebb34702a2c)
or from an Airflow DAG.
3. Once we run the script, we could search the `films` table using Amundsen Search.