Ingestion Framework External Deployment

Any tool capable of running Python code can be used to configure the metadata extraction from your sources.

We have support for Python versions 3.8-3.11

1. How does the Ingestion Framework work?

The Ingestion Framework contains all the logic about how to connect to the sources, extract their metadata and send it to the OpenMetadata server. We have built it from scratch with the main idea of making it an independent component that can be run from - literally - anywhere.

In order to install it, you just need to get it from PyPI.

We will show further examples later, but a piece of code is the best showcase for its simplicity. In order to run a full ingestion process, you just need to execute a single function. For example, if we wanted to run the metadata ingestion from within a simple Python script:

Where this function runs is completely up to you, and you can adapt it to what makes the most sense within your organization and engineering context. Below you'll see some examples of different orchestrators you can leverage to execute the ingestion process.

2. Ingestion Configuration

In the example above, the Workflow class got created from a YAML configuration. Any Workflow that you execute (ingestion, profiler, lineage,...) will have its own YAML representation.

You can think about this configuration as the recipe you want to execute: where is your source, which pieces do you extract, how are they processed and where are they sent.

An example YAML config for extracting MySQL metadata looks like this:

You will find examples of all the workflow's YAML files at each Connector page.

We will now show you examples on how to configure and run every workflow externally by using Snowflake as an example. But first, let's digest some information that will be common everywhere, the workflowConfig.

Workflow Config

Here you will define information such as where are you hosting the OpenMetadata server, and the JWT token to authenticate.

Review this section carefully to ensure you are properly managing service credentials and other security configurations.

Logger Level

You can specify the loggerLevel depending on your needs. If you are trying to troubleshoot an ingestion, running with DEBUG will give you far more traces for identifying issues.

JWT Token

JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration.

Store Service Connection

If set to true (default), we will store the sensitive information either encrypted via the Fernet Key in the database or externally, if you have configured any Secrets Manager.

If set to false, the service will be created, but the service connection information will only be used by the Ingestion Framework at runtime, and won't be sent to the OpenMetadata server.

Secrets Manager Configuration

If you have configured any Secrets Manager, you need to let the Ingestion Framework know how to retrieve the credentials securely.

Follow the docs to configure the secret retrieval based on your environment.

SSL Configuration

If you have added SSL to the OpenMetadata server, then you will need to handle the certificates when running the ingestion too. You can either set verifySSL to ignore, or have it as validate, which will require you to set the sslConfig.caCertificate with a local path where your ingestion runs that points to the server certificate file.

Find more information on how to troubleshoot SSL issues here.

JWT Token with Secrets Manager

If you are using the Secrets Manager, you can let the Ingestion client to pick up the JWT Token dynamically from the Secrets Manager at runtime. Let's show an example:

We have an OpenMetadata server running with the managed-aws Secrets Manager. Since we used the OPENMETADATA_CLUSTER_NAME env var as test, our ingestion-bot JWT Token is safely stored under the secret ID /test/bot/ingestion-bot/config/jwttoken.

Now, we can use the following workflow config to run the ingestion without having to pass the token, but just pointing to the secret itself:

Notice how:

We specify the secretsManagerProvider pointing to aws, since that's the manager we are using.
We set secretsManagerLoader as env. Since we're running this from our local, we'll let the AWS credentials to be loaded from the local env vars. (When running this using the UI, note that the generated workflows will have this value set as airflow!)
We set the jwtToken value as secret:/test/bot/ingestion-bot/config/jwttoken, which tells the client that this value is a secret located under /test/bot/ingestion-bot/config/jwttoken.

Those are our env vars:

And we can run this normally with metadata ingest -c <path to yaml>.

Note that even if you are not using the Secrets Manager for the OpenMetadata Server, you can still apply the same approach by storing the JWT token manually to the secrets manager, and let the Ingestion client pick it up from there automatically.

3. (Optional) Ingestion Pipeline

Additionally, if you want to see your runs logged in the Ingestions tab of the connectors page in the UI as you would when running the connectors natively with OpenMetadata, you can add the following configuration on your YAMLs:

Adding the ingestionPipelineFQN - the Ingestion Pipeline Fully Qualified Name - will tell the Ingestion Framework to log the executions and update the ingestion status, which will appear on the UI. Note that the action buttons will be disabled, since OpenMetadata won't be able to interact with external systems.

4. (Optional) Disable the Pipeline Service Client

If you want to run your workflows ONLY externally without relying on OpenMetadata for any workflow management or scheduling, you can update the following server configuration:

by setting enabled: false or setting the PIPELINE_SERVICE_CLIENT_ENABLED=false as an environment variable.

This will stop certain APIs and monitors related to the Pipeline Service Client (e.g., Airflow) from being operative.

Examples

This is not an exhaustive list, and it will keep growing over time. Not because the orchestrators X or Y are not supported, but just because we did not have the time yet to add it here. If you'd like to chip in and help us expand these guides and examples, don't hesitate to reach to us in Slack or directly open a PR in GitHub.

Airflow

Run the ingestion process externally from Airflow

MWAA

Run the ingestion process externally using AWS MWAA

GCS Composer

Run the ingestion process externally from GCS Composer

GitHub Actions

Run the ingestion process externally from GitHub Actions

Let's jump now into some examples on how you could create the function the run the different workflows. Note that this code can then be executed inside a DAG, a GitHub action, or a vanilla Python script. It will work for any environment.

Testing

You can easily test every YAML configuration using the metadata CLI from the Ingestion Framework. In order to install it, you just need to get it from PyPI.

In each of the examples below, we'll showcase how to run the CLI, assuming you have a YAML file that contains the workflow configuration.

Metadata Workflow

This is the first workflow you have to configure and run. It will take care of fetching the metadata from your sources, be it Database Services, Dashboard Services, Pipelines, etc.

The rest of the workflows (Lineage, Profiler,...) will be executed on top of the metadata already available in the platform.

Adding the imports

The first step is to import the MetadataWorkflow class, which will take care of the full ingestion logic. We'll add the import for printing the results at the end.

Defining the YAML

Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can read from a file, parse secrets from your environment, or any other approach you'd need. In the end, it's just Python code.

You can find complete YAMLs in each connector docs and find more information about the available configurations.

Preparing the Workflow

Finally, we'll prepare a function that we can execute anywhere.

It will take care of instantiating the workflow, executing it and giving us the results.

ingestion.py

You can test the workflow via metadata ingest -c <path-to-yaml>.

Lineage Workflow

This workflow will take care of scanning your query history and defining lineage relationships between your tables.

You can find more information about this workflow here.

Adding the imports

The first step is to import the MetadataWorkflow class, which will take care of the full ingestion logic. We'll add the import for printing the results at the end.

Note that we are using the same class as in the Metadata Ingestion.

Defining the YAML

Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can read from a file, parse secrets from your environment, or any other approach you'd need.

Note how we have not added here the serviceConnection. Since the service would have been created during the metadata ingestion, we can let the Ingestion Framework dynamically fetch the Service Connection information.

If, however, you are configuring the workflow with storeServiceConnection: false, you'll need to explicitly define the serviceConnection.

You can find complete YAMLs in each connector docs and find more information about the available configurations.

Preparing the Workflow

Finally, we'll prepare a function that we can execute anywhere.

It will take care of instantiating the workflow, executing it and giving us the results.

ingestion.py

You can test the workflow via metadata ingest -c <path-to-yaml>.

Usage Workflow

As with the lineage workflow, we'll scan the query history for any DML statements. The goal is to ingest queries into the platform, figure out the relevancy of your assets and frequently joined tables.

Adding the imports

The first step is to import the UsageWorkflow class, which will take care of the full ingestion logic. We'll add the import for printing the results at the end.

Defining the YAML

Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can read from a file, parse secrets from your environment, or any other approach you'd need.

If, however, you are configuring the workflow with storeServiceConnection: false, you'll need to explicitly define the serviceConnection.

You can find complete YAMLs in each connector docs and find more information about the available configurations.

Preparing the Workflow

Finally, we'll prepare a function that we can execute anywhere.

It will take care of instantiating the workflow, executing it and giving us the results.

ingestion.py

You can test the workflow via metadata usage -c <path-to-yaml>.

Profiler Workflow

This workflow will execute queries against your database and send the results into OpenMetadata. The goal is to compute metrics about your data and give you a high-level view of its shape, together with the sample data.

This is an interesting previous step before creating Data Quality Workflows.

You can find more information about this workflow here.

Adding the imports

The first step is to import the ProfilerWorkflow class, which will take care of the full ingestion logic. We'll add the import for printing the results at the end.

Defining the YAML

Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can read from a file, parse secrets from your environment, or any other approach you'd need.

If, however, you are configuring the workflow with storeServiceConnection: false, you'll need to explicitly define the serviceConnection.

You can find complete YAMLs in each connector docs and find more information about the available configurations.

Preparing the Workflow

Finally, we'll prepare a function that we can execute anywhere.

It will take care of instantiating the workflow, executing it and giving us the results.

ingestion.py

You can test the workflow via metadata profile -c <path-to-yaml>.

Data Quality Workflow

This is an interesting previous step before creating Data Quality Workflows.

You can find more information about this workflow here.

Adding the imports

The first step is to import the TestSuiteWorkflow class, which will take care of the full ingestion logic. We'll add the import for printing the results at the end.

Defining the YAML

Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can read from a file, parse secrets from your environment, or any other approach you'd need.

If, however, you are configuring the workflow with storeServiceConnection: false, you'll need to explicitly define the serviceConnection.

Moreover, see how we are not configuring any tests in the processor. You can do that, but even if nothing gets defined in the YAML, we will execute all the tests configured against the table.

You can find complete YAMLs in each connector docs and find more information about the available configurations.

Preparing the Workflow

Finally, we'll prepare a function that we can execute anywhere.

It will take care of instantiating the workflow, executing it and giving us the results.

ingestion.py

You can test the workflow via metadata test -c <path-to-yaml>.