description:Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
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
...
@@ -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)
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:
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)),
- 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
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.
...
@@ -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`.
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
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.
or from an Airflow DAG.
3. Once we run the script, we could search the `films` table using Amundsen Search.
3. Once we run the script, we could search the `films` table using Amundsen Search.