Yes, you can define each Airbyte sync as a software-defined asset in Dagster using the

load_assets_from_connections

or

load_assets_from_airbyte_instance

functions from the

dagster_airbyte

library. This approach treats each Airbyte connection as a Dagster asset, allowing you to specify dependencies between them. Here’s an example of how to define two Airbyte syncs as assets, with the second depending on the first:

python from dagster_airbyte import load_assets_from_connections, AirbyteResource from dagster import Definitions, AssetKey  # Define your Airbyte instance airbyte_instance = AirbyteResource(     host="localhost",     port="8000", )  # Define the two Airbyte connections as assets first_connection = {"connection_id": "first-connection-uuid"} second_connection = {"connection_id": "second-connection-uuid"}  first_asset = load_assets_from_connections(     airbyte=airbyte_instance,     connections=[first_connection],     key_prefix=["airbyte"], )  second_asset = load_assets_from_connections(     airbyte=airbyte_instance,     connections=[second_connection],     key_prefix=["airbyte"],     # Specify dependency on the first asset     upstream_assets={AssetKey(["airbyte", "first-connection-uuid"])} )  defs = Definitions(     assets=[first_asset, second_asset] )

In this setup: - Each Airbyte connection is loaded as a Dagster asset. - The second asset (

second_asset

) is configured to depend on the first asset by specifying

upstream_assets

. - When you materialize the second asset in Dagster, it will ensure the first sync runs and completes successfully before starting the second. For more details and examples, see the [official Airbyte tutorial on configuring Airbyte connections as Dagster assets](https://airbyte.com/tutorials/configure-airbyte-with-python-dagster) and the [AI stack tutorial](https://airbyte.com/tutorials/ai-stack-with-airbyte-langchain-and-dagster). > Note: The exact parameter for specifying dependencies (

upstream_assets

) may require adjustment based on your Dagster version and the API. The knowledge sources provide the general approach, but do not show a full example with explicit asset dependencies between Airbyte connections. If you need more advanced dependency management, you may need to consult the [Dagster documentation](https://docs.dagster.io/concepts/assets/software-defined-assets) for the latest API details.

To run two Airbyte syncs as assets together in Dagster, you define them as assets (as shown previously), and then use the Dagster UI to materialize them, or trigger materialization via the Dagster CLI. When assets are defined with dependencies, Dagster will automatically run them in the correct order—ensuring the dependent asset only runs after its upstream asset completes. How to run both assets together: 1. Start Dagster Launch the Dagster UI with:

dagster dev

2. Access the Dagster UI Open your browser and go to [http://127.0.0.1:3000](http://127.0.0.1:3000). You should see your Airbyte assets listed. 3. Materialize Assets - In the Dagster UI, you can select both assets and click "Materialize". - If you select the downstream asset (the one that depends on the first), Dagster will automatically materialize both in the correct order. This process is described in several tutorials, where after defining Airbyte assets, you use the Dagster UI to "Materialize all" or select specific assets to run. Dagster handles the orchestration and dependencies for you, so you do not need to define a separate job for running the assets together—materializing the downstream asset will trigger the upstream one as needed [Building an E-commerce Data Pipeline: A Hands-On Guide to Using Airbyte, dbt, Dagster, and BigQuery](https://airbyte.com/tutorials/building-an-e-commerce-data-pipeline-a-hands-on-guide-to-using-airbyte-dbt-dagster-and-bigquery), [Customer Segmentation Analytics Stack With Shopify, Airbyte, dbt, Dagster and BigQuery](https://airbyte.com/tutorials/customer-segmentation-analytics-stack-with-shopify-airbyte-dbt-dagster-and-bigquery). If you prefer the CLI, you can use Dagster's asset materialization commands, but the UI is the most straightforward way to trigger and monitor asset runs. > The knowledge sources do not provide a code example for running these assets as a job, but they do confirm that materializing assets in the Dagster UI is the standard approach.

Based on the knowledge sources, the standard and recommended approach in Dagster for orchestrating Airbyte syncs as assets is to define them as software-defined assets and then use the Dagster UI or CLI to materialize them, letting Dagster handle dependencies automatically. The knowledge sources do not provide an explicit example of packaging multiple Airbyte assets into a single custom job using the job API together with asset definitions. Instead, they show that asset orchestration is managed through asset definitions and materialization, not through explicit job definitions for assets. For example, you define your Airbyte assets like this:

python from dagster_airbyte import load_assets_from_connections, AirbyteResource  airbyte_instance = AirbyteResource(     host="localhost",     port="8000", )  first_asset = load_assets_from_connections(     airbyte=airbyte_instance,     connections=[{"connection_id": "first-connection-uuid"}],     key_prefix=["airbyte"], )  second_asset = load_assets_from_connections(     airbyte=airbyte_instance,     connections=[{"connection_id": "second-connection-uuid"}],     key_prefix=["airbyte"],     # (Dependency management is handled by Dagster if you set up upstream assets) )  from dagster import Definitions  defs = Definitions(     assets=[first_asset, second_asset] )

Once these assets are defined, you use the Dagster UI to materialize them, and Dagster will handle the execution order based on dependencies [Configure Airbyte Connections with Python (Dagster)](https://airbyte.com/tutorials/configure-airbyte-with-python-dagster). There is no example in the knowledge sources of wrapping these assets in a custom job and then including that job in Definitions. The asset-based orchestration is the recommended and documented approach. If you need to run both assets together, you simply materialize the downstream asset (or both) in the Dagster UI, and Dagster will ensure the correct execution order. If you need more advanced orchestration (such as custom jobs that include assets), the knowledge sources do not provide enough information to show how to do this with Airbyte assets. Summary: I'm sorry, but the knowledge sources do not contain enough information to show how to package two Airbyte sync assets into a custom Dagster job and include that job in a Definitions object. The documented approach is to define Airbyte syncs as assets and use Dagster's asset materialization features to orchestrate them.

Dagster’s asset materialization features allow you to orchestrate and manage the execution of data assets—such as Airbyte syncs—by treating each asset as a node in a data pipeline. When you define Airbyte connections as assets in Dagster, you can visualize, trigger, and monitor their execution directly from the Dagster UI. How asset materialization orchestrates Airbyte assets: 1. Asset Definition: Each Airbyte sync is defined as a Dagster asset using functions like

load_assets_from_connections

. This makes each sync a first-class citizen in Dagster’s asset graph. 2. Dependency Management: If you specify dependencies between assets (for example, making one Airbyte sync depend on another), Dagster will understand the execution order. When you materialize the downstream asset, Dagster will automatically materialize any upstream assets first. 3. Materialization via UI: In the Dagster UI, you can select one or more assets and click "Materialize". If you select the downstream asset, Dagster will trigger the upstream asset(s) first, ensuring the correct order of execution. This is described in multiple tutorials, where you are instructed to "Materialize all" or select specific assets, and Dagster handles the orchestration for you [Building an E-commerce Data Pipeline: A Hands-On Guide to Using Airbyte, dbt, Dagster, and BigQuery](https://airbyte.com/tutorials/building-an-e-commerce-data-pipeline-a-hands-on-guide-to-using-airbyte-dbt-dagster-and-bigquery), [Customer Segmentation Analytics Stack With Shopify, Airbyte, dbt, Dagster and BigQuery](https://airbyte.com/tutorials/customer-segmentation-analytics-stack-with-shopify-airbyte-dbt-dagster-and-bigquery). 4. Lineage Visualization: The UI also provides a "global asset lineage" view, letting you see how your Airbyte assets (and any other assets, like dbt models) are connected and how data flows through your pipeline. 5. No Custom Job Required: You do not need to define a custom job to orchestrate asset execution. Dagster’s asset materialization system will handle the orchestration based on the dependencies you define between assets. Summary: By defining your Airbyte syncs as assets and specifying dependencies, Dagster’s asset materialization features will automatically orchestrate their execution in the correct order when you trigger materialization from the UI or CLI. This approach is the standard and recommended way to manage Airbyte syncs in Dagster according to the knowledge sources.

To schedule your Airbyte pipeline (with assets defined in Dagster) to run every hour, you should set the schedule in Dagster, not in Airbyte. According to the knowledge sources, when using Dagster to orchestrate Airbyte syncs, you should set your Airbyte connections to manual frequency, so Dagster is responsible for triggering the syncs on your desired schedule. Here’s what you should do: 1. Set Airbyte Connections to Manual: In the Airbyte UI, set the sync frequency for your connections to "Manual". This ensures Airbyte does not run syncs on its own schedule, but waits for Dagster to trigger them ([Using the Dagster Integration](https://docs.airbyte.com/platform/operator-guides/using-dagster-integration)). 2. Define a Schedule in Dagster: The knowledge sources do not provide a direct code example for scheduling asset materializations in Dagster, but they do confirm that orchestration and scheduling should be handled by Dagster when using the integration. In practice, you would define a schedule in Dagster to materialize your assets every hour. 3. Materialization: With the schedule in place, Dagster will trigger the materialization of your Airbyte assets (and their dependencies) every hour, orchestrating the pipeline as you defined. Summary: - Set Airbyte syncs to manual. - Use Dagster’s scheduling features to run the pipeline every hour. > The knowledge sources do not provide a step-by-step code example for scheduling asset materializations, but they clearly state that scheduling should be managed by Dagster when using the integration, and Airbyte connections should be set to manual ([Using the Dagster Integration](https://docs.airbyte.com/platform/operator-guides/using-dagster-integration)).

To confirm that your Airbyte connection actually ran when triggered by Dagster, you should check the Airbyte UI for the connection’s job history and logs. After Dagster triggers a sync, Airbyte records the full logs for each sync attempt. Here’s how you can verify the sync: 1. Go to the Airbyte UI. 2. Navigate to your connection’s page. 3. Open the “Job History” tab to see the latest syncs and their statuses. 4. View logs for a specific sync: - Click the three grey dots next to a sync and select “View logs” to open the in-app log viewer. - You can also download the logs or copy a link to the job for sharing or further inspection. These logs will show you detailed information about the sync, including whether it started, completed, or encountered any errors. If the sync was triggered by Dagster, you should see a new sync attempt corresponding to the time Dagster ran the job ([Browsing logs](https://docs.airbyte.com/platform/operator-guides/browsing-output-logs)). If you want to confirm that data was actually moved, you can also check your destination (e.g., database, data warehouse, or file) for new or updated data as expected ([Set up a Connection – Check the data from your first sync](https://docs.airbyte.com/platform/using-airbyte/getting-started/set-up-a-connection#connection-overview)).

@kapa.ai The dagster pipeline is not checking to ensure airbyte sync was successful, how can I check for the airbyte success?

To ensure that your Dagster pipeline checks whether the Airbyte sync was successful, you should use the built-in Airbyte integration ops provided by Dagster, such as

airbyte_sync_op

. According to the knowledge sources, when you use

airbyte_sync_op

in your Dagster job, it will trigger the Airbyte sync and wait until it completes. The op will fail if the Airbyte sync fails, causing the Dagster job to fail as well. This means that the success or failure of the Airbyte sync is automatically checked by Dagster when using this op. Here’s an example from the documentation:

python from dagster import job   from dagster_airbyte import airbyte_resource, airbyte_sync_op      my_airbyte_resource = airbyte_resource.configured(       {           "host": {"env": "AIRBYTE_HOST"},           "port": {"env": "AIRBYTE_PORT"},       }   )   sync_foobar = airbyte_sync_op.configured({"connection_id": "your-connection-uuid"}, name="sync_foobar")      @job(resource_defs={"airbyte": my_airbyte_resource})   def my_simple_airbyte_job():       sync_foobar()

When this job runs, Dagster will only mark the op as successful if the Airbyte sync completes successfully. If the sync fails, the op (and thus the job) will fail, and you can see this in the Dagster UI or logs. This behavior is described in the [official Dagster integration documentation](https://docs.airbyte.com/platform/operator-guides/using-dagster-integration). If you are using software-defined assets (SDAs) with Airbyte, the same principle applies: materializing the asset will fail if the underlying Airbyte sync fails. Summary: If you use Dagster’s Airbyte integration ops (

airbyte_sync_op

or assets loaded via

dagster_airbyte

), Dagster will automatically check for Airbyte sync success and fail the pipeline if the sync fails. You do not need to add extra checks—this is handled for you by the integration. If you want to see more details, you can always check the Airbyte job logs in the Airbyte UI for further troubleshooting [Using the Dagster Integration](https://docs.airbyte.com/platform/operator-guides/using-dagster-integration).

@kapa.ai Can you rewrite it to also include authorization to airbyte?

Certainly! To include authorization when connecting Dagster to Airbyte, you need to provide the Airbyte username and password in the resource configuration. The knowledge sources show how to do this using the

airbyte_resource

in Dagster, where you can specify the

username

and

password

fields. The password can be securely provided via an environment variable (e.g.,

AIRBYTE_PASSWORD

). Here’s how you can rewrite the example to include authorization:

python from dagster import job from dagster_airbyte import airbyte_resource, airbyte_sync_op  my_airbyte_resource = airbyte_resource.configured(     {         "host": "localhost",         "port": "8000",         "username": "airbyte",         "password": {"env": "AIRBYTE_PASSWORD"},     } )  sync_foobar = airbyte_sync_op.configured(     {"connection_id": "your-connection-uuid"}, name="sync_foobar" )  @job(resource_defs={"airbyte": my_airbyte_resource}) def my_simple_airbyte_job():     sync_foobar()

Note: - Set the

AIRBYTE_PASSWORD

environment variable on your machine before running Dagster. - The default username is usually

airbyte

, and the default password is

password

for local instances, but use your actual credentials if they differ. This approach is demonstrated in the [Configure Airbyte Connections with Python (Dagster)](https://airbyte.com/tutorials/configure-airbyte-with-python-dagster) tutorial.

I'm sorry, but the knowledge sources do not provide explicit instructions or tools for validating Airbyte connection authentication directly from Dagster or via a dedicated validation command. However, the documentation does confirm that Airbyte authentication is based on the email and password provided at setup, and you can retrieve or reset your credentials using the

abctl local credentials

command if you are running Airbyte locally. If you are running Airbyte on Kubernetes, you can retrieve the credentials from the

airbyte-auth-secrets

secret and decode them as described in the documentation [Authentication](https://docs.airbyte.com/platform/deploying-airbyte/integrations/authentication). If you are receiving an "unauthorized" message, it is recommended to: - Double-check that you are using the correct username and password. - Retrieve the current credentials using the appropriate method for your deployment. - Ensure that the credentials are correctly passed to Dagster, for example, via environment variables. If the credentials are correct and you still receive an unauthorized error, you may want to try logging into the Airbyte UI directly with those credentials to confirm they work outside of Dagster. Unfortunately, the knowledge sources do not mention a specific "validate connection" feature for authentication beyond these steps.

To connect to the Airbyte OSS public APIs and list your workspaces, you need to make a request to the

/api/public/v1/workspaces

endpoint on your EC2 machine where Airbyte is running. Steps: 1. Ensure Airbyte is accessible: Make sure the

airbyte-server

service is running and accessible from your network. If you are running Airbyte on EC2, you may need to open the appropriate ports in your security group. 2. Obtain an access token: For Airbyte OSS, you need to authenticate using an access token. First, create an application in the Airbyte UI (Settings → Account → Applications) to get a

client_id

and

client_secret

. Then, request an access token:

bash    curl -X POST "http://<YOUR_EC2_PUBLIC_IP>:8000/api/v1/applications/token" \      -H "Content-Type: application/json" \      -d '{"client_id": "<YOUR_CLIENT_ID>", "client_secret": "<YOUR_CLIENT_SECRET>"}'

The response will include an

access_token

. 3. List workspaces using the public API: Use the access token to call the public API endpoint:

bash    curl -X GET "http://<YOUR_EC2_PUBLIC_IP>:8000/api/public/v1/workspaces" \      -H "Authorization: Bearer <YOUR_ACCESS_TOKEN>" \      -H "Content-Type: application/json"

This will return a list of your workspaces. Note: - Replace

<YOUR_EC2_PUBLIC_IP>

,

<YOUR_CLIENT_ID>

,

<YOUR_CLIENT_SECRET>

, and

<YOUR_ACCESS_TOKEN>

with your actual values. - The

/api/public/v1

path prefix is required for OSS users [API documentation](https://docs.airbyte.com/platform/api-documentation). For more details, see the official documentation on [Configuring API Access](https://docs.airbyte.com/platform/using-airbyte/configuring-api-access) and [API documentation](https://docs.airbyte.com/platform/api-documentation).

Yes, you can use your localhost port to access the Airbyte OSS public APIs if you have an SSH tunnel set up to your EC2 instance. This is a common approach when your Airbyte instance is not directly exposed to the internet. For example, if you have set up your SSH tunnel like this:

bash ssh -L 8000:<ec2-private-ip>:8000 ec2-user@<ec2-public-ip> -i <your-key.pem>

You can then make API requests to

<http://localhost:8000>

on your local machine, and they will be forwarded to the Airbyte server running on your EC2 instance. So, to list your workspaces, you would: 1. Obtain an access token:

bash    curl -X POST "<http://localhost:8000/api/v1/applications/token>" \      -H "Content-Type: application/json" \      -d '{"client_id": "<YOUR_CLIENT_ID>", "client_secret": "<YOUR_CLIENT_SECRET>"}'

2. List workspaces:

bash    curl -X GET "<http://localhost:8000/api/public/v1/workspaces>" \      -H "Authorization: Bearer <YOUR_ACCESS_TOKEN>" \      -H "Content-Type: application/json"

This approach is confirmed in the knowledge sources, where users set up SSH tunnels to access the Airbyte UI and APIs via

localhost

on their local machine [Octavia CLI connect to Airbyte instance through bastion host](https://discuss.airbyte.io/t/1546), [Unable to access Airbyte UI](https://discuss.airbyte.io/t/1314). Just ensure your SSH tunnel is active and mapped to the correct port.

Yes, you can trigger a specific Airbyte connection using the public API by specifying the

connectionId

in your API request. After setting up your SSH tunnel and obtaining an access token as described earlier, you can trigger a sync for a specific connection with the following command:

bash curl -X POST "<http://localhost:8000/api/v1/connections/sync>" \   -H "Authorization: Bearer <YOUR_ACCESS_TOKEN>" \   -H "Content-Type: application/json" \   -d '{"connectionId":"<YOUR_CONNECTION_ID>"}'

Replace

<YOUR_ACCESS_TOKEN>

with the token you obtained, and

<YOUR_CONNECTION_ID>

with the ID of the Airbyte connection you want to trigger. You can find the connection ID in the Airbyte UI URL when viewing the connection details. This method is confirmed in the tutorials, which show how to trigger a sync for a specific connection using the API by providing the

connectionId

in the request body [How to use Airflow and Airbyte together](https://airbyte.com/tutorials/how-to-use-airflow-and-airbyte-together).

Yes, tokens generated using the Airbyte API do expire after generation. According to the documentation, when you obtain an

access_token

using your

client_id

and

client_secret

, the token is a Bearer Token that is valid for a limited time: - In most recent Airbyte OSS and Self-Managed deployments, the

access_token

is valid for 3 minutes. After it expires, you must request a new token from the

/api/v1/applications/token

endpoint to continue making API requests ([Using Airbyte: Configuring API Access](https://docs.airbyte.com/platform/using-airbyte/configuring-api-access)). - In some Self-Managed Enterprise deployments, the token may be valid for 60 minutes ([Enterprise Setup: API Access](https://docs.airbyte.com/platform/enterprise-setup/api-access-config)). Always check the documentation for your specific Airbyte version and deployment type to confirm the token lifetime. > Access tokens are short-lived (3 minutes) so we recommend fetching a new token before each request. > — [Using Airbyte: Configuring API Access](https://docs.airbyte.com/platform/using-airbyte/configuring-api-access) Once the token expires, you must generate a new one to continue accessing the API.