Hybrid SaaS

There's two options on how to set up a data connector:

Run the connector in Collate SaaS: In this scenario, you'll get an IP when you add the service. You need to give access to this IP in your data sources.
Run the connector in your infrastructure or laptop: In this case, Collate won't be accessing the data, but rather you'd control where and how the process is executed and Collate will only receive the output of the metadata extraction. This is an interesting option for sources lying behind private networks or when external SaaS services are not allowed to connect to your data sources.

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

In this section we'll show you how the ingestion process works and how to test it from your laptop.

Collate Ingestion Agent

The Collate Ingestion Agent is designed to facilitate metadata ingestion for hybrid deployments, allowing organizations to securely push metadata from their infrastructure into the Collate platform without exposing their internal systems. It provides a secure and efficient channel for running ingestion workflows while maintaining full control over data processing within your network. This document outlines the setup and usage of the Collate Ingestion Agent, emphasizing its role in hybrid environments and key functionalities.

Overview

The Collate Ingestion Agent is ideal for scenarios where running connectors on-premises is necessary, providing a secure and efficient way to process metadata within your infrastructure. This eliminates concerns about data privacy and streamlines the ingestion process.

With the Collate Ingestion Agent, you can:

Set up ingestion workflows easily without configuring YAML files manually.
Leverage the Collate UI for a seamless and user-friendly experience.
Manage various ingestion types, including metadata, profiling, lineage, usage, DBT, and data quality.

Setting Up the Collate Ingestion Agent

1. Prepare Your Environment

To begin, download the Collate-provided Docker image for the Ingestion Agent. The Collate team will provide the necessary credentials to authenticate and pull the image from the repository.

Run the following commands:

Log in to Docker: Use the credentials provided by Collate to authenticate.
Pull the Docker Image: Run the command to pull the image into your local environment.

Once the image is downloaded, you can start the Docker container to initialize the Ingestion Agent.

2. Configure the Agent

Access the Local Agent UI:

Open your browser and navigate to the local instance of the Collate Ingestion Agent.

Set Up the Connection:

Enter your Collate platform URL (e.g., https://<your-company>.collate.com/api).
Add the ingestion bot token from the Collate settings under Settings > Bots > Ingestion Bot.

Verify Services:

Open the Collate UI and confirm that all available services (e.g., databases) are visible in the Ingestion Agent interface.

3. Add a New Service

Navigate to the Database Services section in the Ingestion Agent UI.
Click Add New Service and select the database type (e.g., Redshift).
Enter the necessary service configuration:
- Service Name: A unique name for the database service.
- Host and Port: Connection details for the database.
- Username and Password: Credentials to access the database.
- Database Name: The target database for ingestion.
Test the connection to ensure the service is properly configured.

4. Run Metadata Ingestion

After creating the service, navigate to the Ingestion tab and click Add Ingestion.
Select the ingestion type (e.g., metadata) and specify any additional configurations:
- Include specific schemas or tables.
- Enable options like DDL inclusion if required.
Choose whether to:
- Run the ingestion immediately via the agent.
- Download the YAML configuration file for running ingestion on an external scheduler.
Monitor the logs in real-time to track the ingestion process.

5. Verify Ingested Data

Return to the Collate platform and refresh the database service.
Verify that the ingested metadata, including schemas, tables, and column details, is available.
Explore additional ingestion options like profiling, lineage, or data quality for the service.

Additional Features

The Collate Ingestion Agent supports various ingestion workflows, allowing you to:

Generate YAML Configurations: Download YAML files for external scheduling.
Manage Ingestion Types: Run metadata, profiling, lineage, usage, and other workflows as needed.
Monitor Progress: View logs and monitor real-time ingestion activity.

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.

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>.