## Source Code and Repositories
diff --git a/datahub-web-react/README.md b/datahub-web-react/README.md
index 6c91b169af858..8bf592b11a0ae 100644
--- a/datahub-web-react/README.md
+++ b/datahub-web-react/README.md
@@ -126,7 +126,9 @@ for functional configurability should reside.
to render a view associated with a particular entity type (user, dataset, etc.).
-
+
+
+
**graphql** - The React App talks to the `dathub-frontend` server using GraphQL. This module is where the *queries* issued
against the server are defined. Once defined, running `yarn run generate` will code-gen TypeScript objects to make invoking
diff --git a/docker/airflow/local_airflow.md b/docker/airflow/local_airflow.md
index d0a2b18cff2d2..55a64f5c122c5 100644
--- a/docker/airflow/local_airflow.md
+++ b/docker/airflow/local_airflow.md
@@ -138,25 +138,57 @@ Successfully added `conn_id`=datahub_rest_default : datahub_rest://:@http://data
Navigate the Airflow UI to find the sample Airflow dag we just brought in
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+
+
+
+
+
By default, Airflow loads all DAG-s in paused status. Unpause the sample DAG to use it.
-
-
+
+
+
+
+
+
+
+
+
+
Then trigger the DAG to run.
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+
+
+
+
+
After the DAG runs successfully, go over to your DataHub instance to see the Pipeline and navigate its lineage.
-
-
+
+
## TroubleShooting
@@ -164,9 +196,17 @@ Most issues are related to connectivity between Airflow and DataHub.
Here is how you can debug them.
-
-
+
+
+
+
+
+
+
+
+
+
In this case, clearly the connection `datahub-rest` has not been registered. Looks like we forgot to register the connection with Airflow!
Let's execute Step 4 to register the datahub connection with Airflow.
@@ -175,4 +215,8 @@ In case the connection was registered successfully but you are still seeing `Fai
After re-running the DAG, we see success!
-
+
+
+
**In the Actions Framework, Events flow continuously from left-to-right.**
### Pipelines
diff --git a/docs/advanced/no-code-modeling.md b/docs/advanced/no-code-modeling.md
index 9c8f6761a62bc..d76b776d3dddb 100644
--- a/docs/advanced/no-code-modeling.md
+++ b/docs/advanced/no-code-modeling.md
@@ -159,11 +159,19 @@ along with simplifying the number of raw data models that need defined, includin
From an architectural PoV, we will move from a before that looks something like this:
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+
+
+
+
+
to an after that looks like this
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+
+
+
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+
That is, a move away from patterns of strong-typing-everywhere to a more generic + flexible world.
diff --git a/docs/api/graphql/how-to-set-up-graphql.md b/docs/api/graphql/how-to-set-up-graphql.md
index 562e8edb9f5d9..584bf34ad3f92 100644
--- a/docs/api/graphql/how-to-set-up-graphql.md
+++ b/docs/api/graphql/how-to-set-up-graphql.md
@@ -62,7 +62,11 @@ Postman is a popular API client that provides a graphical user interface for sen
Within Postman, you can create a `POST` request and set the request URL to the `/api/graphql` endpoint.
In the request body, select the `GraphQL` option and enter your GraphQL query in the request body.
-
+
+
+
+
+
Please refer to [Querying with GraphQL](https://learning.postman.com/docs/sending-requests/graphql/graphql/) in the Postman documentation for more information.
diff --git a/docs/api/tutorials/custom-properties.md b/docs/api/tutorials/custom-properties.md
index dbc07bfaa712e..fe0d7e62dcde8 100644
--- a/docs/api/tutorials/custom-properties.md
+++ b/docs/api/tutorials/custom-properties.md
@@ -34,7 +34,11 @@ In this example, we will add some custom properties `cluster_name` and `retentio
After you have ingested sample data, the dataset `fct_users_deleted` should have a custom properties section with `encoding` set to `utf-8`.
-
+
+
+
+
+
```shell
datahub get --urn "urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)" --aspect datasetProperties
@@ -80,7 +84,11 @@ The following code adds custom properties `cluster_name` and `retention_time` to
You can now see the two new properties are added to `fct_users_deleted` and the previous property `encoding` is unchanged.
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+
+
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+
We can also verify this operation by programmatically checking the `datasetProperties` aspect after running this code using the `datahub` cli.
@@ -130,7 +138,11 @@ The following code shows you how can add and remove custom properties in the sam
You can now see the `cluster_name` property is added to `fct_users_deleted` and the `retention_time` property is removed.
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+
+
+
+
+
We can also verify this operation programmatically by checking the `datasetProperties` aspect using the `datahub` cli.
@@ -179,7 +191,11 @@ The following code replaces the current custom properties with a new properties
You can now see the `cluster_name` and `retention_time` properties are added to `fct_users_deleted` but the previous `encoding` property is no longer present.
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+
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+
We can also verify this operation programmatically by checking the `datasetProperties` aspect using the `datahub` cli.
diff --git a/docs/api/tutorials/datasets.md b/docs/api/tutorials/datasets.md
index 62b30e97c8020..7c6d4a88d4190 100644
--- a/docs/api/tutorials/datasets.md
+++ b/docs/api/tutorials/datasets.md
@@ -42,7 +42,11 @@ For detailed steps, please refer to [Datahub Quickstart Guide](/docs/quickstart.
You can now see `realestate_db.sales` dataset has been created.
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+
+
+
+
+
## Delete Dataset
@@ -110,4 +114,8 @@ Expected Response:
The dataset `fct_users_deleted` has now been deleted, so if you search for a hive dataset named `fct_users_delete`, you will no longer be able to see it.
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+
+
+
+
+
diff --git a/docs/api/tutorials/deprecation.md b/docs/api/tutorials/deprecation.md
index 6a8f7c8a1d2be..73e73f5224cbc 100644
--- a/docs/api/tutorials/deprecation.md
+++ b/docs/api/tutorials/deprecation.md
@@ -155,4 +155,8 @@ Expected Response:
You can now see the dataset `fct_users_created` has been marked as `Deprecated.`
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+
+
+
+
diff --git a/docs/api/tutorials/descriptions.md b/docs/api/tutorials/descriptions.md
index 46f42b7a05be6..27c57309ba76a 100644
--- a/docs/api/tutorials/descriptions.md
+++ b/docs/api/tutorials/descriptions.md
@@ -275,7 +275,11 @@ Expected Response:
You can now see the description is added to `fct_users_deleted`.
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+
+
+
+
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## Add Description on Column
@@ -357,4 +361,8 @@ Expected Response:
You can now see column description is added to `user_name` column of `fct_users_deleted`.
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+
+
+
+
diff --git a/docs/api/tutorials/domains.md b/docs/api/tutorials/domains.md
index c8c47f85c570f..617864d233b7a 100644
--- a/docs/api/tutorials/domains.md
+++ b/docs/api/tutorials/domains.md
@@ -74,7 +74,11 @@ Expected Response:
You can now see `Marketing` domain has been created under `Govern > Domains`.
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+
+
+
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## Read Domains
@@ -209,7 +213,11 @@ Expected Response:
You can now see `Marketing` domain has been added to the dataset.
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+
+
+
+
+
## Remove Domains
@@ -259,4 +267,8 @@ curl --location --request POST 'http://localhost:8080/api/graphql' \
You can now see a domain `Marketing` has been removed from the `fct_users_created` dataset.
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+
+
+
+
+
diff --git a/docs/api/tutorials/lineage.md b/docs/api/tutorials/lineage.md
index e37986af7bbbd..ce23a4d274e8e 100644
--- a/docs/api/tutorials/lineage.md
+++ b/docs/api/tutorials/lineage.md
@@ -112,7 +112,11 @@ Expected Response:
You can now see the lineage between `fct_users_deleted` and `logging_events`.
-
+
+
+
+
+
## Add Column-level Lineage
@@ -130,7 +134,11 @@ You can now see the lineage between `fct_users_deleted` and `logging_events`.
You can now see the column-level lineage between datasets. Note that you have to enable `Show Columns` to be able to see the column-level lineage.
-
+
+
+
+
+
## Read Lineage
diff --git a/docs/api/tutorials/ml.md b/docs/api/tutorials/ml.md
index b16f2669b30c7..cb77556d48ebf 100644
--- a/docs/api/tutorials/ml.md
+++ b/docs/api/tutorials/ml.md
@@ -94,9 +94,17 @@ Please note that an MlModelGroup serves as a container for all the runs of a sin
You can search the entities in DataHub UI.
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-
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+
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+
+
+
+
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## Read ML Entities
@@ -499,6 +507,14 @@ Expected Response: (Note that this entity does not exist in the sample ingestion
You can access to `Features` or `Group` Tab of each entity to view the added entities.
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diff --git a/docs/api/tutorials/owners.md b/docs/api/tutorials/owners.md
index 3c7a46b136d76..5bc3b95cb5631 100644
--- a/docs/api/tutorials/owners.md
+++ b/docs/api/tutorials/owners.md
@@ -77,7 +77,11 @@ Update succeeded for urn urn:li:corpuser:datahub.
### Expected Outcomes of Upserting User
You can see the user `The bar` has been created and the user `Datahub` has been updated under `Settings > Access > Users & Groups`
-
+
+
+
+
+
## Upsert Group
@@ -125,7 +129,11 @@ Update succeeded for group urn:li:corpGroup:foogroup@acryl.io.
### Expected Outcomes of Upserting Group
You can see the group `Foo Group` has been created under `Settings > Access > Users & Groups`
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+
+
+
+
+
## Read Owners
@@ -272,7 +280,11 @@ curl --location --request POST 'http://localhost:8080/api/graphql' \
You can now see `bfoo` has been added as an owner to the `fct_users_created` dataset.
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+
+
+
+
+
## Remove Owners
@@ -340,4 +352,8 @@ curl --location --request POST 'http://localhost:8080/api/graphql' \
You can now see `John Doe` has been removed as an owner from the `fct_users_created` dataset.
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diff --git a/docs/api/tutorials/tags.md b/docs/api/tutorials/tags.md
index 2f80a833136c1..b2234bf00bcb9 100644
--- a/docs/api/tutorials/tags.md
+++ b/docs/api/tutorials/tags.md
@@ -91,7 +91,11 @@ Expected Response:
You can now see the new tag `Deprecated` has been created.
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+
+
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+
We can also verify this operation by programmatically searching `Deprecated` tag after running this code using the `datahub` cli.
@@ -307,7 +311,11 @@ Expected Response:
You can now see `Deprecated` tag has been added to `user_name` column.
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We can also verify this operation programmatically by checking the `globalTags` aspect using the `datahub` cli.
@@ -359,7 +367,11 @@ curl --location --request POST 'http://localhost:8080/api/graphql' \
You can now see `Deprecated` tag has been removed to `user_name` column.
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+
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We can also verify this operation programmatically by checking the `gloablTags` aspect using the `datahub` cli.
diff --git a/docs/api/tutorials/terms.md b/docs/api/tutorials/terms.md
index 207e14ea4afe8..99acf77d26ab0 100644
--- a/docs/api/tutorials/terms.md
+++ b/docs/api/tutorials/terms.md
@@ -95,7 +95,11 @@ Expected Response:
You can now see the new term `Rate of Return` has been created.
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+
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+
We can also verify this operation by programmatically searching `Rate of Return` term after running this code using the `datahub` cli.
@@ -289,7 +293,11 @@ Expected Response:
You can now see `Rate of Return` term has been added to `user_name` column.
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+
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## Remove Terms
@@ -361,4 +369,8 @@ curl --location --request POST 'http://localhost:8080/api/graphql' \
You can now see `Rate of Return` term has been removed to `user_name` column.
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diff --git a/docs/architecture/architecture.md b/docs/architecture/architecture.md
index 6b76b995cc427..6a9c1860d71b0 100644
--- a/docs/architecture/architecture.md
+++ b/docs/architecture/architecture.md
@@ -10,8 +10,16 @@ disparate tools & systems.
The figures below describe the high-level architecture of DataHub.
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For a more detailed look at the components that make up the Architecture, check out [Components](../components.md).
diff --git a/docs/architecture/metadata-ingestion.md b/docs/architecture/metadata-ingestion.md
index 2b60383319c68..abf8fc24d1385 100644
--- a/docs/architecture/metadata-ingestion.md
+++ b/docs/architecture/metadata-ingestion.md
@@ -6,7 +6,11 @@ title: "Ingestion Framework"
DataHub supports an extremely flexible ingestion architecture that can support push, pull, asynchronous and synchronous models.
The figure below describes all the options possible for connecting your favorite system to DataHub.
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+
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+
+
+
## Metadata Change Proposal: The Center Piece
diff --git a/docs/architecture/metadata-serving.md b/docs/architecture/metadata-serving.md
index ada41179af4e0..57194f49d5ea4 100644
--- a/docs/architecture/metadata-serving.md
+++ b/docs/architecture/metadata-serving.md
@@ -6,7 +6,11 @@ title: "Serving Tier"
The figure below shows the high-level system diagram for DataHub's Serving Tier.
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The primary component is called [the Metadata Service](../../metadata-service) and exposes a REST API and a GraphQL API for performing CRUD operations on metadata. The service also exposes search and graph query API-s to support secondary-index style queries, full-text search queries as well as relationship queries like lineage. In addition, the [datahub-frontend](../../datahub-frontend) service expose a GraphQL API on top of the metadata graph.
diff --git a/docs/authentication/concepts.md b/docs/authentication/concepts.md
index 715e94c7e0380..0940f86a805f1 100644
--- a/docs/authentication/concepts.md
+++ b/docs/authentication/concepts.md
@@ -11,7 +11,11 @@ We introduced a few important concepts to the Metadata Service to make authentic
In following sections, we'll take a closer look at each individually.
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*High level overview of Metadata Service Authentication*
## What is an Actor?
diff --git a/docs/authentication/guides/sso/configure-oidc-react-azure.md b/docs/authentication/guides/sso/configure-oidc-react-azure.md
index d185957967882..177387327c0e8 100644
--- a/docs/authentication/guides/sso/configure-oidc-react-azure.md
+++ b/docs/authentication/guides/sso/configure-oidc-react-azure.md
@@ -32,7 +32,11 @@ Azure supports more than one redirect URI, so both can be configured at the same
At this point, your app registration should look like the following:
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+
e. Click **Register**.
@@ -40,7 +44,11 @@ e. Click **Register**.
Once registration is done, you will land on the app registration **Overview** tab. On the left-side navigation bar, click on **Authentication** under **Manage** and add extra redirect URIs if need be (if you want to support both local testing and Azure deployments).
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Click **Save**.
@@ -51,7 +59,11 @@ Select **Client secrets**, then **New client secret**. Type in a meaningful des
**IMPORTANT:** Copy the `value` of your newly create secret since Azure will never display its value afterwards.
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+
+
+
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+
### 4. Configure API permissions
@@ -66,7 +78,11 @@ Click on **Add a permission**, then from the **Microsoft APIs** tab select **Mic
At this point, you should be looking at a screen like the following:
-
+
+
+
+
+
### 5. Obtain Application (Client) ID
diff --git a/docs/authentication/guides/sso/configure-oidc-react-google.md b/docs/authentication/guides/sso/configure-oidc-react-google.md
index 474538097aae2..af62185e6e787 100644
--- a/docs/authentication/guides/sso/configure-oidc-react-google.md
+++ b/docs/authentication/guides/sso/configure-oidc-react-google.md
@@ -31,7 +31,11 @@ Note that in order to complete this step you should be logged into a Google acco
c. Fill out the details in the App Information & Domain sections. Make sure the 'Application Home Page' provided matches where DataHub is deployed
at your organization.
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+
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+
Once you've completed this, **Save & Continue**.
@@ -70,7 +74,11 @@ f. You will now receive a pair of values, a client id and a client secret. Bookm
At this point, you should be looking at a screen like the following:
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+
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+
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+
Success!
diff --git a/docs/authentication/guides/sso/configure-oidc-react-okta.md b/docs/authentication/guides/sso/configure-oidc-react-okta.md
index cfede999f1e70..320b887a28f16 100644
--- a/docs/authentication/guides/sso/configure-oidc-react-okta.md
+++ b/docs/authentication/guides/sso/configure-oidc-react-okta.md
@@ -69,8 +69,16 @@ for example, `https://dev-33231928.okta.com/.well-known/openid-configuration`.
At this point, you should be looking at a screen like the following:
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+
+
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Success!
@@ -96,7 +104,11 @@ Replacing the placeholders above with the client id & client secret received fro
>
> By default, we assume that the groups will appear in a claim named "groups". This can be customized using the `AUTH_OIDC_GROUPS_CLAIM` container configuration.
>
-> 
+>
+
+
+
+
### 5. Restart `datahub-frontend-react` docker container
diff --git a/docs/authentication/guides/sso/img/azure-setup-api-permissions.png b/docs/authentication/guides/sso/img/azure-setup-api-permissions.png
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diff --git a/docs/authentication/guides/sso/img/okta-setup-1.png b/docs/authentication/guides/sso/img/okta-setup-1.png
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diff --git a/docs/authentication/guides/sso/img/okta-setup-2.png b/docs/authentication/guides/sso/img/okta-setup-2.png
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diff --git a/docs/authentication/guides/sso/img/okta-setup-groups-claim.png b/docs/authentication/guides/sso/img/okta-setup-groups-claim.png
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diff --git a/docs/authentication/personal-access-tokens.md b/docs/authentication/personal-access-tokens.md
index 0188aab49444e..dc57a989a4e0c 100644
--- a/docs/authentication/personal-access-tokens.md
+++ b/docs/authentication/personal-access-tokens.md
@@ -71,7 +71,11 @@ curl 'http://localhost:8080/entities/urn:li:corpuser:datahub' -H 'Authorization:
Since authorization happens at the GMS level, this means that ingestion is also protected behind access tokens, to use them simply add a `token` to the sink config property as seen below:
-
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+
+
+
:::note
diff --git a/docs/components.md b/docs/components.md
index ef76729bb37fb..b59dabcf999cc 100644
--- a/docs/components.md
+++ b/docs/components.md
@@ -6,7 +6,11 @@ title: "Components"
The DataHub platform consists of the components shown in the following diagram.
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## Metadata Store
diff --git a/docs/demo/DataHub-UIOverview.pdf b/docs/demo/DataHub-UIOverview.pdf
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diff --git a/docs/demo/Town_Hall_Presentation_-_12-2020_-_UI_Development_Part_2.pdf b/docs/demo/Town_Hall_Presentation_-_12-2020_-_UI_Development_Part_2.pdf
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diff --git a/docs/demo/ViasatMetadataJourney.pdf b/docs/demo/ViasatMetadataJourney.pdf
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diff --git a/docs/deploy/aws.md b/docs/deploy/aws.md
index 7b01ffa02a744..228fcb51d1a28 100644
--- a/docs/deploy/aws.md
+++ b/docs/deploy/aws.md
@@ -201,7 +201,11 @@ Provision a MySQL database in AWS RDS that shares the VPC with the kubernetes cl
the VPC of the kubernetes cluster. Once the database is provisioned, you should be able to see the following page. Take
a note of the endpoint marked by the red box.
-
+
+
+
+
+
First, add the DB password to kubernetes by running the following.
@@ -234,7 +238,11 @@ Provision an elasticsearch domain running elasticsearch version 7.10 or above th
cluster or has VPC peering set up between the VPC of the kubernetes cluster. Once the domain is provisioned, you should
be able to see the following page. Take a note of the endpoint marked by the red box.
-
+
+
+
+
+
Update the elasticsearch settings under global in the values.yaml as follows.
@@ -330,7 +338,11 @@ Provision an MSK cluster that shares the VPC with the kubernetes cluster or has
the kubernetes cluster. Once the domain is provisioned, click on the “View client information” button in the ‘Cluster
Summary” section. You should see a page like below. Take a note of the endpoints marked by the red boxes.
-
+
+
+
+
+
Update the kafka settings under global in the values.yaml as follows.
diff --git a/docs/deploy/confluent-cloud.md b/docs/deploy/confluent-cloud.md
index d93ffcceaecee..794b55d4686bf 100644
--- a/docs/deploy/confluent-cloud.md
+++ b/docs/deploy/confluent-cloud.md
@@ -24,7 +24,11 @@ decommissioned.
To create the topics, navigate to your **Cluster** and click "Create Topic". Feel free to tweak the default topic configurations to
match your preferences.
-
+
+
+
+
+
## Step 2: Configure DataHub Container to use Confluent Cloud Topics
@@ -140,12 +144,20 @@ and another for the user info used for connecting to the schema registry. You'll
select "Clients" -> "Configure new Java Client". You should see a page like the following:
-
+
+
+
+
+
You'll want to generate both a Kafka Cluster API Key & a Schema Registry key. Once you do so,you should see the config
automatically populate with your new secrets:
-
+
+
+
+
+
You'll need to copy the values of `sasl.jaas.config` and `basic.auth.user.info`
for the next step.
diff --git a/docs/deploy/gcp.md b/docs/deploy/gcp.md
index 3713d69f90636..0cd3d92a8f3cd 100644
--- a/docs/deploy/gcp.md
+++ b/docs/deploy/gcp.md
@@ -65,16 +65,28 @@ the GKE page on [GCP website](https://console.cloud.google.com/kubernetes/discov
Once all deploy is successful, you should see a page like below in the "Services & Ingress" tab on the left.
-
+
+
+
+
+
Tick the checkbox for datahub-datahub-frontend and click "CREATE INGRESS" button. You should land on the following page.
-
+
+
+
+
+
Type in an arbitrary name for the ingress and click on the second step "Host and path rules". You should land on the
following page.
-
+
+
+
+
+
Select "datahub-datahub-frontend" in the dropdown menu for backends, and then click on "ADD HOST AND PATH RULE" button.
In the second row that got created, add in the host name of choice (here gcp.datahubproject.io) and select
@@ -83,14 +95,22 @@ In the second row that got created, add in the host name of choice (here gcp.dat
This step adds the rule allowing requests from the host name of choice to get routed to datahub-frontend service. Click
on step 3 "Frontend configuration". You should land on the following page.
-
+
+
+
+
+
Choose HTTPS in the dropdown menu for protocol. To enable SSL, you need to add a certificate. If you do not have one,
you can click "CREATE A NEW CERTIFICATE" and input the host name of choice. GCP will create a certificate for you.
Now press "CREATE" button on the left to create ingress! After around 5 minutes, you should see the following.
-
+
+
+
+
+
In your domain provider, add an A record for the host name set above using the IP address on the ingress page (noted
with the red box). Once DNS updates, you should be able to access DataHub through the host name!!
@@ -98,5 +118,9 @@ with the red box). Once DNS updates, you should be able to access DataHub throug
Note, ignore the warning icon next to ingress. It takes about ten minutes for ingress to check that the backend service
is ready and show a check mark as follows. However, ingress is fully functional once you see the above page.
-
+
+
+
+
+
diff --git a/docs/dev-guides/timeline.md b/docs/dev-guides/timeline.md
index 966e659b90991..829aef1d3eefa 100644
--- a/docs/dev-guides/timeline.md
+++ b/docs/dev-guides/timeline.md
@@ -14,7 +14,11 @@ The Timeline API is available in server versions `0.8.28` and higher. The `cli`
## Entity Timeline Conceptually
For the visually inclined, here is a conceptual diagram that illustrates how to think about the entity timeline with categorical changes overlaid on it.
-
+
+
+
+
+
## Change Event
Each modification is modeled as a
@@ -228,8 +232,16 @@ http://localhost:8080/openapi/timeline/v1/urn%3Ali%3Adataset%3A%28urn%3Ali%3Adat
The API is browse-able via the UI through through the dropdown.
Here are a few screenshots showing how to navigate to it. You can try out the API and send example requests.
-
-
+
+
+
+
+
+
+
+
+
+
# Future Work
diff --git a/docs/docker/development.md b/docs/docker/development.md
index 2153aa9dc613f..91a303744a03b 100644
--- a/docs/docker/development.md
+++ b/docs/docker/development.md
@@ -92,7 +92,11 @@ Environment variables control the debugging ports for GMS and the frontend.
The screenshot shows an example configuration for IntelliJ using the default GMS debugging port of 5001.
-
+
+
+
+
+
## Tips for People New To Docker
diff --git a/docs/glossary/business-glossary.md b/docs/glossary/business-glossary.md
index faab6f12fc55e..e10cbed30b913 100644
--- a/docs/glossary/business-glossary.md
+++ b/docs/glossary/business-glossary.md
@@ -31,59 +31,103 @@ In order to view a Business Glossary, users must have the Platform Privilege cal
Once granted this privilege, you can access your Glossary by clicking the dropdown at the top of the page called **Govern** and then click **Glossary**:
-
+
+
+
+
+
You are now at the root of your Glossary and should see all Terms and Term Groups with no parents assigned to them. You should also notice a hierarchy navigator on the left where you can easily check out the structure of your Glossary!
-
+
+
+
+
+
## Creating a Term or Term Group
There are two ways to create Terms and Term Groups through the UI. First, you can create directly from the Glossary home page by clicking the menu dots on the top right and selecting your desired option:
-
+
+
+
+
+
You can also create Terms or Term Groups directly from a Term Group's page. In order to do that you need to click the menu dots on the top right and select what you want:
-
+
+
+
+
+
Note that the modal that pops up will automatically set the current Term Group you are in as the **Parent**. You can easily change this by selecting the input and navigating through your Glossary to find your desired Term Group. In addition, you could start typing the name of a Term Group to see it appear by searching. You can also leave this input blank in order to create a Term or Term Group with no parent.
-
+
+
+
+
+
## Editing a Term or Term Group
In order to edit a Term or Term Group, you first need to go the page of the Term or Term group you want to edit. Then simply click the edit icon right next to the name to open up an inline editor. Change the text and it will save when you click outside or hit Enter.
-
+
+
+
+
+
## Moving a Term or Term Group
Once a Term or Term Group has been created, you can always move it to be under a different Term Group parent. In order to do this, click the menu dots on the top right of either entity and select **Move**.
-
+
+
+
+
+
This will open a modal where you can navigate through your Glossary to find your desired Term Group.
-
+
+
+
+
+
## Deleting a Term or Term Group
In order to delete a Term or Term Group, you need to go to the entity page of what you want to delete then click the menu dots on the top right. From here you can select **Delete** followed by confirming through a separate modal. **Note**: at the moment we only support deleting Term Groups that do not have any children. Until cascade deleting is supported, you will have to delete all children first, then delete the Term Group.
-
+
+
+
+
+
## Adding a Term to an Entity
Once you've defined your Glossary, you can begin attaching terms to data assets. To add a Glossary Term to an asset, go to the entity page of your asset and find the **Add Terms** button on the right sidebar.
-
+
+
+
+
+
In the modal that pops up you can select the Term you care about in one of two ways:
- Search for the Term by name in the input
- Navigate through the Glossary dropdown that appears after clicking into the input
-
+
+
+
+
+
## Privileges
diff --git a/docs/how/configuring-authorization-with-apache-ranger.md b/docs/how/configuring-authorization-with-apache-ranger.md
index 26d3be6d358b2..46f9432e6c18a 100644
--- a/docs/how/configuring-authorization-with-apache-ranger.md
+++ b/docs/how/configuring-authorization-with-apache-ranger.md
@@ -67,7 +67,11 @@ Now, you should have the DataHub plugin registered with Apache Ranger. Next, we'
**DATAHUB** plugin and **ranger_datahub** service is shown in below screenshot:
- 
+
+
+
+
+
4. Create a new policy under service **ranger_datahub** - this will be used to control DataHub authorization.
5. Create a test user & assign them to a policy. We'll use the `datahub` user, which is the default root user inside DataHub.
@@ -80,7 +84,11 @@ Now, you should have the DataHub plugin registered with Apache Ranger. Next, we'
DataHub platform access policy screenshot:
- 
+
+
+
+
+
Once we've created our first policy, we can set up DataHub to start authorizing requests using Ranger policies.
@@ -178,7 +186,11 @@ then follow the below sections to undo the configuration steps you have performe
**ranger_datahub** service is shown in below screenshot:
- 
+
+
+
+
+
2. Delete **datahub** plugin: Execute below curl command to delete **datahub** plugin
Replace variables with corresponding values in curl command
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diff --git a/docs/imgs/timeline/swagger-ui.png b/docs/imgs/timeline/swagger-ui.png
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diff --git a/docs/imgs/timeline/timeline-conceptually.png b/docs/imgs/timeline/timeline-conceptually.png
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diff --git a/docs/imgs/user-sign-up-screen.png b/docs/imgs/user-sign-up-screen.png
deleted file mode 100644
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diff --git a/docs/links.md b/docs/links.md
index f175262b9b5d9..45ba391e557cd 100644
--- a/docs/links.md
+++ b/docs/links.md
@@ -39,7 +39,7 @@
* [Creating Notebook-based Dynamic Dashboards](https://towardsdatascience.com/creating-notebook-based-dynamic-dashboards-91f936adc6f3)
## Talks & Presentations
-* [DataHub: Powering LinkedIn's Metadata](demo/DataHub_-_Powering_LinkedIn_Metadata.pdf) @ [Budapest Data Forum 2020](https://budapestdata.hu/2020/en/)
+* [DataHub: Powering LinkedIn's Metadata](https://github.com/acryldata/static-assets-test/raw/master/imgs/demo/DataHub_-_Powering_LinkedIn_Metadata.pdf) @ [Budapest Data Forum 2020](https://budapestdata.hu/2020/en/)
* [Taming the Data Beast Using DataHub](https://www.youtube.com/watch?v=bo4OhiPro7Y) @ [Data Engineering Melbourne Meetup November 2020](https://www.meetup.com/Data-Engineering-Melbourne/events/kgnvlrybcpbjc/)
* [Metadata Management And Integration At LinkedIn With DataHub](https://www.dataengineeringpodcast.com/datahub-metadata-management-episode-147/) @ [Data Engineering Podcast](https://www.dataengineeringpodcast.com)
* [The evolution of metadata: LinkedIn’s story](https://speakerdeck.com/shirshanka/the-evolution-of-metadata-linkedins-journey-strata-nyc-2019) @ [Strata Data Conference 2019](https://conferences.oreilly.com/strata/strata-ny-2019.html)
diff --git a/docs/managed-datahub/chrome-extension.md b/docs/managed-datahub/chrome-extension.md
index a614327c7fd29..c6840f4e8e221 100644
--- a/docs/managed-datahub/chrome-extension.md
+++ b/docs/managed-datahub/chrome-extension.md
@@ -10,7 +10,11 @@ import FeatureAvailability from '@site/src/components/FeatureAvailability';
In order to use the Acryl DataHub Chrome extension, you need to download it onto your browser from the Chrome web store [here](https://chrome.google.com/webstore/detail/datahub-chrome-extension/aoenebhmfokhglijmoacfjcnebdpchfj).
-
+
+
+
+
+
Simply click "Add to Chrome" then "Add extension" on the ensuing popup.
@@ -20,11 +24,19 @@ Once you have your extension installed, you'll need to configure it to work with
1. Click the extension button on the right of your browser's address bar to view all of your installed extensions. Click on the newly installed DataHub extension.
-
+
+
+
+
+
2. Fill in your DataHub domain and click "Continue" in the extension popup that appears.
-
+
+
+
+
+
If your organization uses standard SaaS domains for Looker, you should be ready to go!
@@ -34,11 +46,19 @@ Some organizations have custom SaaS domains for Looker and some Acryl DataHub de
1. Click on the extension button and select your DataHub extension to open the popup again. Now click the settings icon in order to open the configurations page.
-
+
+
+
+
+
2. Fill out any and save custom configurations you have in the **TOOL CONFIGURATIONS** section. Here you can configure a custom domain, a Platform Instance associated with that domain, and the Environment set on your DataHub assets. If you don't have a custom domain but do have a custom Platform Instance or Environment, feel free to leave the field domain empty.
-
+
+
+
+
+
## Using the Extension
@@ -52,7 +72,11 @@ Once you have everything configured on your extension, it's time to use it!
4. Click the Acryl DataHub extension button on the bottom right of your page to open a drawer where you can now see additional information about this asset right from your DataHub instance.
-
+
+
+
+
+
## Advanced: Self-Hosted DataHub
diff --git a/docs/managed-datahub/datahub-api/graphql-api/getting-started.md b/docs/managed-datahub/datahub-api/graphql-api/getting-started.md
index 3c57b0a21d96e..57d46f05c4e0c 100644
--- a/docs/managed-datahub/datahub-api/graphql-api/getting-started.md
+++ b/docs/managed-datahub/datahub-api/graphql-api/getting-started.md
@@ -10,7 +10,11 @@ For a full reference to the Queries & Mutations available for consumption, check
### Connecting to the API
-.png)
+
+
+
+
+
When you generate the token you will see an example of `curl` command which you can use to connect to the GraphQL API.
diff --git a/docs/managed-datahub/datahub-api/graphql-api/incidents-api-beta.md b/docs/managed-datahub/datahub-api/graphql-api/incidents-api-beta.md
index 89bacb2009e49..bfd8e8f2dae1b 100644
--- a/docs/managed-datahub/datahub-api/graphql-api/incidents-api-beta.md
+++ b/docs/managed-datahub/datahub-api/graphql-api/incidents-api-beta.md
@@ -404,7 +404,11 @@ You can configure Acryl to send slack notifications to a specific channel when i
These notifications are also able to tag the immediate asset's owners, along with the owners of downstream assets consuming it.
-
+
+
+
+
+
To do so, simply follow the [Slack Integration Guide](docs/managed-datahub/saas-slack-setup.md) and contact your Acryl customer success team to enable the feature!
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diff --git a/docs/managed-datahub/integrations/oidc-sso-integration.md b/docs/managed-datahub/integrations/oidc-sso-integration.md
index 6a9e085186b44..c0f5069d849fa 100644
--- a/docs/managed-datahub/integrations/oidc-sso-integration.md
+++ b/docs/managed-datahub/integrations/oidc-sso-integration.md
@@ -42,4 +42,8 @@ To enable the OIDC integration, start by navigating to **Settings > Platform > S
4. If there are any advanced settings you would like to configure, click on the **Advanced** button. These come with defaults, so only input settings here if there is something you need changed from the default configuration.
5. Click **Update** to save your settings.
-.png)
+
+
+
+
+
diff --git a/docs/managed-datahub/metadata-ingestion-with-acryl/ingestion.md b/docs/managed-datahub/metadata-ingestion-with-acryl/ingestion.md
index 95ca6e5e33e16..e225fd8b014c8 100644
--- a/docs/managed-datahub/metadata-ingestion-with-acryl/ingestion.md
+++ b/docs/managed-datahub/metadata-ingestion-with-acryl/ingestion.md
@@ -56,9 +56,17 @@ In Acryl DataHub deployments, you _must_ use a sink of type `datahub-rest`, whic
2. **token**: a unique API key used to authenticate requests to your instance's REST API
The token can be retrieved by logging in as admin. You can go to Settings page and generate a Personal Access Token with your desired expiration date.
-.png)
-
+
+
+
+
+
+
+
+
+
+
To configure your instance of DataHub as the destination for ingestion, set the "server" field of your recipe to point to your Acryl instance's domain suffixed by the path `/gms`, as shown below.
A complete example of a DataHub recipe file, which reads from MySQL and writes into a DataHub instance:
diff --git a/docs/managed-datahub/operator-guide/setting-up-remote-ingestion-executor-on-aws.md b/docs/managed-datahub/operator-guide/setting-up-remote-ingestion-executor-on-aws.md
index d389ec97d0550..6c6cce51ea098 100644
--- a/docs/managed-datahub/operator-guide/setting-up-remote-ingestion-executor-on-aws.md
+++ b/docs/managed-datahub/operator-guide/setting-up-remote-ingestion-executor-on-aws.md
@@ -17,11 +17,19 @@ Acryl DataHub comes packaged with an Acryl-managed ingestion executor, which is
For example, if an ingestion source is not publicly accessible via the internet, e.g. hosted privately within a specific AWS account, then the Acryl executor will be unable to extract metadata from it.
-.png)
+
+
+
+
+
To accommodate these cases, Acryl supports configuring a remote ingestion executor which can be deployed inside of your AWS account. This setup allows you to continue leveraging the Acryl DataHub console to create, schedule, and run metadata ingestion, all while retaining network and credential isolation.
-.png)
+
+
+
+
+
## Deploying a Remote Ingestion Executor
1. **Provide AWS Account Id**: Provide Acryl Team with the id of the AWS in which the remote executor will be hosted. This will be used to grant access to private Acryl containers and create a unique SQS queue which your remote agent will subscribe to. The account id can be provided to your Acryl representative via Email or [One Time Secret](https://onetimesecret.com/).
@@ -40,23 +48,39 @@ To accommodate these cases, Acryl supports configuring a remote ingestion execut
Note that the only external secret provider that is currently supported is AWS Secrets Manager.
-
-
+
+
+
+
+
+
+
+
+
+
3. **Test the Executor:** To test your remote executor:
1. Create a new Ingestion Source by clicking '**Create new Source**' the '**Ingestion**' tab of the DataHub console. Configure your Ingestion Recipe as though you were running it from inside of your environment.
2. When working with "secret" fields (passwords, keys, etc), you can refer to any "self-managed" secrets by name: `${SECRET_NAME}:`
- 
+
+
+
+
+
3. In the 'Finish Up' step, click '**Advanced'**.
4. Update the '**Executor Id**' form field to be '**remote**'. This indicates that you'd like to use the remote executor.
5. Click '**Done**'.
Now, simple click '**Execute**' to test out the remote executor. If your remote executor is configured properly, you should promptly see the ingestion task state change to 'Running'.
-
+
+
+
+
+
## Updating a Remote Ingestion Executor
In order to update the executor, ie. to deploy a new container version, you'll need to update the CloudFormation Stack to re-deploy the CloudFormation template with a new set of parameters.
### Steps - AWS Console
@@ -66,7 +90,11 @@ In order to update the executor, ie. to deploy a new container version, you'll n
4. Select **Replace Current Template**
5. Select **Upload a template file**
6. Upload a copy of the Acryl Remote Executor [CloudFormation Template](https://raw.githubusercontent.com/acryldata/datahub-cloudformation/master/Ingestion/templates/python.ecs.template.yaml)
-
+
+
+
+
+
7. Click **Next**
8. Change parameters based on your modifications (e.g. ImageTag, etc)
9. Click **Next**
diff --git a/docs/modeling/extending-the-metadata-model.md b/docs/modeling/extending-the-metadata-model.md
index f47630f44e772..98f70f6d933e4 100644
--- a/docs/modeling/extending-the-metadata-model.md
+++ b/docs/modeling/extending-the-metadata-model.md
@@ -11,7 +11,11 @@ these two concepts prior to making changes.
## To fork or not to fork?
An important question that will arise once you've decided to extend the metadata model is whether you need to fork the main repo or not. Use the diagram below to understand how to make this decision.
-
+
+
+
+
+
The green lines represent pathways that will lead to lesser friction for you to maintain your code long term. The red lines represent higher risk of conflicts in the future. We are working hard to move the majority of model extension use-cases to no-code / low-code pathways to ensure that you can extend the core metadata model without having to maintain a custom fork of DataHub.
diff --git a/docs/modeling/metadata-model.md b/docs/modeling/metadata-model.md
index 704fce1412329..037c9c7108a6e 100644
--- a/docs/modeling/metadata-model.md
+++ b/docs/modeling/metadata-model.md
@@ -30,7 +30,11 @@ Conceptually, metadata is modeled using the following abstractions
Here is an example graph consisting of 3 types of entity (CorpUser, Chart, Dashboard), 2 types of relationship (OwnedBy, Contains), and 3 types of metadata aspect (Ownership, ChartInfo, and DashboardInfo).
-
+
+
+
+
+
## The Core Entities
@@ -73,7 +77,11 @@ to the YAML configuration, instead of creating new Snapshot / Aspect files.
## Exploring DataHub's Metadata Model
To explore the current DataHub metadata model, you can inspect this high-level picture that shows the different entities and edges between them showing the relationships between them.
-
+
+
+
+
+
To navigate the aspect model for specific entities and explore relationships using the `foreign-key` concept, you can view them in our demo environment or navigate the auto-generated docs in the **Metadata Modeling/Entities** section on the left.
diff --git a/docs/platform-instances.md b/docs/platform-instances.md
index c6bfe3315de98..0f4515aedae54 100644
--- a/docs/platform-instances.md
+++ b/docs/platform-instances.md
@@ -1,44 +1,48 @@
-# Working With Platform Instances
-
-DataHub's metadata model for Datasets supports a three-part key currently:
-- Data Platform (e.g. urn:li:dataPlatform:mysql)
-- Name (e.g. db.schema.name)
-- Env or Fabric (e.g. DEV, PROD, etc.)
-
-This naming scheme unfortunately does not allow for easy representation of the multiplicity of platforms (or technologies) that might be deployed at an organization within the same environment or fabric. For example, an organization might have multiple Redshift instances in Production and would want to see all the data assets located in those instances inside the DataHub metadata repository.
-
-As part of the `v0.8.24+` releases, we are unlocking the first phase of supporting Platform Instances in the metadata model. This is done via two main additions:
-- The `dataPlatformInstance` aspect that has been added to Datasets which allows datasets to be associated to an instance of a platform
-- Enhancements to all ingestion sources that allow them to attach a platform instance to the recipe that changes the generated urns to go from `urn:li:dataset:(urn:li:dataPlatform:,,ENV)` format to `urn:li:dataset:(urn:li:dataPlatform:,,ENV)` format. Sources that produce lineage to datasets in other platforms (e.g. Looker, Superset etc) also have specific configuration additions that allow the recipe author to specify the mapping between a platform and the instance name that it should be mapped to.
-
-
-
-## Naming Platform Instances
-
-When configuring a platform instance, choose an instance name that is understandable and will be stable for the foreseeable future. e.g. `core_warehouse` or `finance_redshift` are allowed names, as are pure guids like `a37dc708-c512-4fe4-9829-401cd60ed789`. Remember that whatever instance name you choose, you will need to specify it in more than one recipe to ensure that the identifiers produced by different sources will line up.
-
-## Enabling Platform Instances
-
-Read the Ingestion source specific guides for how to enable platform instances in each of them.
-The general pattern is to add an additional optional configuration parameter called `platform_instance`.
-
-e.g. here is how you would configure a recipe to ingest a mysql instance that you want to call `core_finance`
-```yaml
-source:
- type: mysql
- config:
- # Coordinates
- host_port: localhost:3306
- platform_instance: core_finance
- database: dbname
-
- # Credentials
- username: root
- password: example
-
-sink:
- # sink configs
-```
-
-
-##
+# Working With Platform Instances
+
+DataHub's metadata model for Datasets supports a three-part key currently:
+- Data Platform (e.g. urn:li:dataPlatform:mysql)
+- Name (e.g. db.schema.name)
+- Env or Fabric (e.g. DEV, PROD, etc.)
+
+This naming scheme unfortunately does not allow for easy representation of the multiplicity of platforms (or technologies) that might be deployed at an organization within the same environment or fabric. For example, an organization might have multiple Redshift instances in Production and would want to see all the data assets located in those instances inside the DataHub metadata repository.
+
+As part of the `v0.8.24+` releases, we are unlocking the first phase of supporting Platform Instances in the metadata model. This is done via two main additions:
+- The `dataPlatformInstance` aspect that has been added to Datasets which allows datasets to be associated to an instance of a platform
+- Enhancements to all ingestion sources that allow them to attach a platform instance to the recipe that changes the generated urns to go from `urn:li:dataset:(urn:li:dataPlatform:,,ENV)` format to `urn:li:dataset:(urn:li:dataPlatform:,,ENV)` format. Sources that produce lineage to datasets in other platforms (e.g. Looker, Superset etc) also have specific configuration additions that allow the recipe author to specify the mapping between a platform and the instance name that it should be mapped to.
+
+
+
+
+
+
+
+## Naming Platform Instances
+
+When configuring a platform instance, choose an instance name that is understandable and will be stable for the foreseeable future. e.g. `core_warehouse` or `finance_redshift` are allowed names, as are pure guids like `a37dc708-c512-4fe4-9829-401cd60ed789`. Remember that whatever instance name you choose, you will need to specify it in more than one recipe to ensure that the identifiers produced by different sources will line up.
+
+## Enabling Platform Instances
+
+Read the Ingestion source specific guides for how to enable platform instances in each of them.
+The general pattern is to add an additional optional configuration parameter called `platform_instance`.
+
+e.g. here is how you would configure a recipe to ingest a mysql instance that you want to call `core_finance`
+```yaml
+source:
+ type: mysql
+ config:
+ # Coordinates
+ host_port: localhost:3306
+ platform_instance: core_finance
+ database: dbname
+
+ # Credentials
+ username: root
+ password: example
+
+sink:
+ # sink configs
+```
+
+
+##
diff --git a/docs/schema-history.md b/docs/schema-history.md
index 9fc9ec1af52bb..120d041960186 100644
--- a/docs/schema-history.md
+++ b/docs/schema-history.md
@@ -23,20 +23,32 @@ must have the **View Entity Page** privilege, or be assigned to **any** DataHub
You can view the Schema History for a Dataset by navigating to that Dataset's Schema Tab. As long as that Dataset has more than
one version, you can view what a Dataset looked like at any given version by using the version selector.
Here's an example from DataHub's official Demo environment with the
-[Snowflake pets dataset](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:snowflake,long_tail_companions.adoption.pets,PROD)/Schema?is_lineage_mode=false).
+Snowflake pets dataset.
+
+
+
+
+
-
If you click on an older version in the selector, you'll be able to see what the schema looked like back then. Notice
the changes here to the glossary terms for the `status` field, and to the descriptions for the `created_at` and `updated_at`
fields.
-
+
+
+
+
+
In addition to this, you can also toggle the Audit view that shows you when the most recent changes were made to each field.
You can active this by clicking on the Audit icon you see above the top right of the table.
-
+
+
+
+
+
You can see here that some of these fields were added at the oldest dataset version, while some were added only at this latest
version. Some fields were even modified and had a type change at the latest version!
diff --git a/docs/townhall-history.md b/docs/townhall-history.md
index 1da490ca6fa69..e235a70c5d7b9 100644
--- a/docs/townhall-history.md
+++ b/docs/townhall-history.md
@@ -343,8 +343,7 @@ Agenda
- Announcements - 2 mins
- Community Updates ([video](https://youtu.be/r862MZTLAJ0?t=99)) - 10 mins
-- Use-Case: DataHub at Viasat ([slides](demo/ViasatMetadataJourney.pdf),[video](https://youtu.be/2SrDAJnzkjE)) by [Anna Kepler](https://www.linkedin.com/in/akepler) - 15 mins
-- Tech Deep Dive: GraphQL + React RFCs readout and discussion ([slides](https://docs.google.com/presentation/d/e/2PACX-1vRtnINnpi6PvFw7-5iW8PSQoT9Kdf1O_0YW7QAr1_mSdJMNftYFTVCjKL-e3fpe8t6IGkha8UpdmoOI/pub?start=false&loop=false&delayms=3000) ,[video](https://www.youtube.com/watch?v=PrBaFrb7pqA)) by [John Joyce](https://www.linkedin.com/in/john-joyce-759883aa) and [Arun Vasudevan](https://www.linkedin.com/in/arun-vasudevan-55117368/) - 15 mins
+- Use-Case: DataHub at Viasat ([slides](https://github.com/acryldata/static-assets-test/raw/master/imgs/demo/ViasatMetadataJourney.pdf),[video](https://youtu.be/2SrDAJnzkjE)) by [Anna Kepler](https://www.linkedin.com/in/akepler) - 15 mins- Tech Deep Dive: GraphQL + React RFCs readout and discussion ([slides](https://docs.google.com/presentation/d/e/2PACX-1vRtnINnpi6PvFw7-5iW8PSQoT9Kdf1O_0YW7QAr1_mSdJMNftYFTVCjKL-e3fpe8t6IGkha8UpdmoOI/pub?start=false&loop=false&delayms=3000) ,[video](https://www.youtube.com/watch?v=PrBaFrb7pqA)) by [John Joyce](https://www.linkedin.com/in/john-joyce-759883aa) and [Arun Vasudevan](https://www.linkedin.com/in/arun-vasudevan-55117368/) - 15 mins
- General Q&A from sign up sheet, slack, and participants - 15 mins
- Closing remarks - 3 mins
- General Q&A from sign up sheet, slack, and participants - 15 mins
@@ -356,8 +355,8 @@ Agenda
Agenda
- Quick intro - 5 mins
-- [Why did Grofers choose DataHub for their data catalog?](demo/Datahub_at_Grofers.pdf) by [Shubham Gupta](https://www.linkedin.com/in/shubhamg931/) - 15 minutes
-- [DataHub UI development - Part 2](demo/Town_Hall_Presentation_-_12-2020_-_UI_Development_Part_2.pdf) by [Charlie Tran](https://www.linkedin.com/in/charlie-tran/) (LinkedIn) - 20 minutes
+- [Why did Grofers choose DataHub for their data catalog?](https://github.com/acryldata/static-assets-test/raw/master/imgs/demo/Datahub_at_Grofers.pdf) by [Shubham Gupta](https://www.linkedin.com/in/shubhamg931/) - 15 minutes
+- [DataHub UI development - Part 2](https://github.com/acryldata/static-assets-test/raw/master/imgs/demo/Town_Hall_Presentation_-_12-2020_-_UI_Development_Part_2.pdf) by [Charlie Tran](https://www.linkedin.com/in/charlie-tran/) (LinkedIn) - 20 minutes
- General Q&A from sign up sheet, slack, and participants - 15 mins
- Closing remarks - 5 minutes
@@ -368,9 +367,9 @@ Agenda
Agenda
- Quick intro - 5 mins
-- [Lightning talk on Metadata use-cases at LinkedIn](demo/Metadata_Use-Cases_at_LinkedIn_-_Lightning_Talk.pdf) by [Shirshanka Das](https://www.linkedin.com/in/shirshankadas/) (LinkedIn) - 5 mins
-- [Strongly Consistent Secondary Index (SCSI) in GMA](demo/Datahub_-_Strongly_Consistent_Secondary_Indexing.pdf), an upcoming feature by [Jyoti Wadhwani](https://www.linkedin.com/in/jyotiwadhwani/) (LinkedIn) - 15 minutes
-- [DataHub UI overview](demo/DataHub-UIOverview.pdf) by [Ignacio Bona](https://www.linkedin.com/in/ignaciobona) (LinkedIn) - 20 minutes
+- [Lightning talk on Metadata use-cases at LinkedIn](https://github.com/acryldata/static-assets-test/raw/master/imgs/demo/Metadata_Use-Cases_at_LinkedIn_-_Lightning_Talk.pdf) by [Shirshanka Das](https://www.linkedin.com/in/shirshankadas/) (LinkedIn) - 5 mins
+- [Strongly Consistent Secondary Index (SCSI) in GMA](https://github.com/acryldata/static-assets-test/raw/master/imgs/demo/Datahub_-_Strongly_Consistent_Secondary_Indexing.pdf), an upcoming feature by [Jyoti Wadhwani](https://www.linkedin.com/in/jyotiwadhwani/) (LinkedIn) - 15 minutes
+- [DataHub UI overview](https://github.com/acryldata/static-assets-test/raw/master/imgs/demo/DataHub-UIOverview.pdf) by [Ignacio Bona](https://www.linkedin.com/in/ignaciobona) (LinkedIn) - 20 minutes
- General Q&A from sign up sheet, slack, and participants - 10 mins
- Closing remarks - 5 minutes
@@ -382,8 +381,8 @@ Agenda
Agenda
- Quick intro - 5 mins
-- [Data Discoverability at SpotHero](demo/Data_Discoverability_at_SpotHero.pdf) by [Maggie Hays](https://www.linkedin.com/in/maggie-hays/) (SpotHero) - 20 mins
-- [Designing the next generation of metadata events for scale](demo/Designing_the_next_generation_of_metadata_events_for_scale.pdf) by [Chris Lee](https://www.linkedin.com/in/chrisleecmu/) (LinkedIn) - 15 mins
+- [Data Discoverability at SpotHero](https://github.com/acryldata/static-assets-test/raw/master/imgs/demo/Data_Discoverability_at_SpotHero.pdf) by [Maggie Hays](https://www.linkedin.com/in/maggie-hays/) (SpotHero) - 20 mins
+- [Designing the next generation of metadata events for scale](https://github.com/acryldata/static-assets-test/raw/master/imgs/demo/Designing_the_next_generation_of_metadata_events_for_scale.pdf) by [Chris Lee](https://www.linkedin.com/in/chrisleecmu/) (LinkedIn) - 15 mins
- General Q&A from sign up sheet, slack, and participants - 15 mins
- Closing remarks - 5 mins
diff --git a/docs/ui-ingestion.md b/docs/ui-ingestion.md
index 4435f66e514f3..2ecb1e634c79f 100644
--- a/docs/ui-ingestion.md
+++ b/docs/ui-ingestion.md
@@ -14,11 +14,19 @@ This document will describe the steps required to configure, schedule, and execu
To view & manage UI-based metadata ingestion, you must have the `Manage Metadata Ingestion` & `Manage Secrets`
privileges assigned to your account. These can be granted by a [Platform Policy](authorization/policies.md).
-
+
+
+
+
+
Once you have these privileges, you can begin to manage ingestion by navigating to the 'Ingestion' tab in DataHub.
-
+
+
+
+
+
On this page, you'll see a list of active **Ingestion Sources**. An Ingestion Sources is a unique source of metadata ingested
into DataHub from an external source like Snowflake, Redshift, or BigQuery.
@@ -33,7 +41,11 @@ your first **Ingestion Source**.
Before ingesting any metadata, you need to create a new Ingestion Source. Start by clicking **+ Create new source**.
-
+
+
+
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#### Step 1: Select a Platform Template
@@ -41,7 +53,11 @@ In the first step, select a **Recipe Template** corresponding to the source type
a variety of natively supported integrations, from Snowflake to Postgres to Kafka.
Select `Custom` to construct an ingestion recipe from scratch.
-
+
+
+
+
+
Next, you'll configure an ingestion **Recipe**, which defines _how_ and _what_ to extract from the source system.
@@ -68,7 +84,11 @@ used by DataHub to extract metadata from a 3rd party system. It most often consi
A sample of a full recipe configured to ingest metadata from MySQL can be found in the image below.
-
+
+
+
+
+
Detailed configuration examples & documentation for each source type can be found on the [DataHub Docs](https://datahubproject.io/docs/metadata-ingestion/) website.
@@ -80,7 +100,11 @@ that are encrypted and stored within DataHub's storage layer.
To create a secret, first navigate to the 'Secrets' tab. Then click `+ Create new secret`.
-
+
+
+
+
+
_Creating a Secret to store the username for a MySQL database_
@@ -123,7 +147,11 @@ Secret values are not persisted to disk beyond execution time, and are never tra
Next, you can optionally configure a schedule on which to execute your new Ingestion Source. This enables to schedule metadata extraction on a monthly, weekly, daily, or hourly cadence depending on the needs of your organization.
Schedules are defined using CRON format.
-
+
+
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_An Ingestion Source that is executed at 9:15am every day, Los Angeles time_
@@ -136,7 +164,11 @@ you can always come back and change this.
Finally, give your Ingestion Source a name.
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Once you're happy with your configurations, click 'Done' to save your changes.
@@ -149,7 +181,11 @@ with the server. However, you can override the default package version using the
To do so, simply click 'Advanced', then change the 'CLI Version' text box to contain the exact version
of the DataHub CLI you'd like to use.
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+
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_Pinning the CLI version to version `0.8.23.2`_
Once you're happy with your changes, simply click 'Done' to save.
@@ -200,11 +236,19 @@ Once you've created your Ingestion Source, you can run it by clicking 'Execute'.
you should see the 'Last Status' column of the ingestion source change from `N/A` to `Running`. This
means that the request to execute ingestion has been successfully picked up by the DataHub ingestion executor.
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+
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If ingestion has executed successfully, you should see it's state shown in green as `Succeeded`.
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### Cancelling an Ingestion Run
@@ -212,14 +256,22 @@ If ingestion has executed successfully, you should see it's state shown in green
If your ingestion run is hanging, there may a bug in the ingestion source, or another persistent issue like exponential timeouts. If these situations,
you can cancel ingestion by clicking **Cancel** on the problematic run.
-
+
+
+
+
+
Once cancelled, you can view the output of the ingestion run by clicking **Details**.
### Debugging a Failed Ingestion Run
-
+
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+
+
A variety of things can cause an ingestion run to fail. Common reasons for failure include:
@@ -235,12 +287,20 @@ A variety of things can cause an ingestion run to fail. Common reasons for failu
4. **Authentication**: If you've enabled [Metadata Service Authentication](authentication/introducing-metadata-service-authentication.md), you'll need to provide a Personal Access Token
in your Recipe Configuration. To so this, set the 'token' field of the sink configuration to contain a Personal Access Token:
- 
+
+
+
+
+
The output of each run is captured and available to view in the UI for easier debugging. To view output logs, click **DETAILS**
on the corresponding ingestion run.
-
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+
+
+
+
## FAQ
@@ -250,7 +310,11 @@ If not due to one of the reasons outlined above, this may be because the executo
to reach DataHub's backend using the default configurations. Try changing your ingestion recipe to make the `sink.config.server` variable point to the Docker
DNS name for the `datahub-gms` pod:
-
+
+
+
+
+
### I see 'N/A' when I try to run ingestion. What do I do?
diff --git a/docs/what/relationship.md b/docs/what/relationship.md
index 1908bbd6ce75f..dcfe093a1b124 100644
--- a/docs/what/relationship.md
+++ b/docs/what/relationship.md
@@ -2,7 +2,11 @@
A relationship is a named associate between exactly two [entities](entity.md), a source and a destination.
-
+
+
+
+
+
From the above graph, a `Group` entity can be linked to a `User` entity via a `HasMember` relationship.
Note that the name of the relationship reflects the direction, i.e. pointing from `Group` to `User`.
diff --git a/metadata-ingestion/adding-source.md b/metadata-ingestion/adding-source.md
index 50e6a1cd5fcc6..e4fc950a7cdbd 100644
--- a/metadata-ingestion/adding-source.md
+++ b/metadata-ingestion/adding-source.md
@@ -44,7 +44,11 @@ class LookerAPIConfig(ConfigModel):
```
generates the following documentation:
-
+
+
+
+
+
:::note
Inline markdown or code snippets are not yet supported for field level documentation.
diff --git a/metadata-ingestion/developing.md b/metadata-ingestion/developing.md
index 67041d23a21b1..5d49b9a866a3d 100644
--- a/metadata-ingestion/developing.md
+++ b/metadata-ingestion/developing.md
@@ -74,7 +74,9 @@ The syntax for installing plugins is slightly different in development. For exam
## Architecture
-
+
+
+
The architecture of this metadata ingestion framework is heavily inspired by [Apache Gobblin](https://gobblin.apache.org/) (also originally a LinkedIn project!). We have a standardized format - the MetadataChangeEvent - and sources and sinks which respectively produce and consume these objects. The sources pull metadata from a variety of data systems, while the sinks are primarily for moving this metadata into DataHub.
diff --git a/metadata-ingestion/docs/dev_guides/stateful.md b/metadata-ingestion/docs/dev_guides/stateful.md
index eccacbb416714..b3a409e965c62 100644
--- a/metadata-ingestion/docs/dev_guides/stateful.md
+++ b/metadata-ingestion/docs/dev_guides/stateful.md
@@ -38,7 +38,9 @@ Following is the list of current use-cases powered by stateful ingestion in data
Stateful ingestion can be used to automatically soft-delete the tables and views that are seen in a previous run
but absent in the current run (they are either deleted or no longer desired).
-
+
+
+
#### Supported sources
* All sql based sources.
diff --git a/metadata-ingestion/docs/sources/azure-ad/azure-ad.md b/metadata-ingestion/docs/sources/azure-ad/azure-ad.md
index 8b375fbee4f33..d2677d7e4fc7a 100644
--- a/metadata-ingestion/docs/sources/azure-ad/azure-ad.md
+++ b/metadata-ingestion/docs/sources/azure-ad/azure-ad.md
@@ -5,6 +5,15 @@ to read your organization's Users and Groups. The following permissions are requ
- `GroupMember.Read.All`
- `User.Read.All`
-You can add a permission by navigating to the permissions tab in your DataHub application on the Azure AD portal. 
+You can add a permission by navigating to the permissions tab in your DataHub application on the Azure AD portal.
+
+
+
-You can view the necessary endpoints to configure by clicking on the Endpoints button in the Overview tab. 
+
+You can view the necessary endpoints to configure by clicking on the Endpoints button in the Overview tab.
+
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+
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+
diff --git a/metadata-ingestion/docs/sources/databricks/README.md b/metadata-ingestion/docs/sources/databricks/README.md
index 01aee3236e01c..b380a892c22b9 100644
--- a/metadata-ingestion/docs/sources/databricks/README.md
+++ b/metadata-ingestion/docs/sources/databricks/README.md
@@ -15,8 +15,11 @@ To complete the picture, we recommend adding push-based ingestion from your Spar
## Watch the DataHub Talk at the Data and AI Summit 2022
For a deeper look at how to think about DataHub within and across your Databricks ecosystem, watch the recording of our talk at the Data and AI Summit 2022.
-
-[](https://www.youtube.com/watch?v=SCP0PR3t7dc)
+
diff --git a/metadata-ingestion/docs/sources/looker/looker_datahub_permission_set.png b/metadata-ingestion/docs/sources/looker/looker_datahub_permission_set.png
deleted file mode 100644
index 7227dc04fb8a0..0000000000000
Binary files a/metadata-ingestion/docs/sources/looker/looker_datahub_permission_set.png and /dev/null differ
diff --git a/metadata-ingestion/docs/sources/looker/looker_pre.md b/metadata-ingestion/docs/sources/looker/looker_pre.md
index ad7fff9c0daaf..6798103d66e99 100644
--- a/metadata-ingestion/docs/sources/looker/looker_pre.md
+++ b/metadata-ingestion/docs/sources/looker/looker_pre.md
@@ -19,7 +19,10 @@ see_user_dashboards
see_users
```
Here is an example permission set after configuration.
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#### Get an API key
diff --git a/perf-test/README.md b/perf-test/README.md
index 24fb064d3e28a..191833361eae9 100644
--- a/perf-test/README.md
+++ b/perf-test/README.md
@@ -58,7 +58,9 @@ locust -f perf-test/locustfiles/ingest.py
This will set up the web interface in http://localhost:8089 (unless the port is already taken). Once you click into it,
you should see the following
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Input the number of users you would like to spawn and the spawn rate. Point the host to the deployed DataHub GMS (
locally, it should be http://localhost:8080). Click on the "Start swarming" button to start the load test.
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