Airbyte & Dagster#

Loading Airbyte asset definitions from an Airbyte instance#

To load Airbyte assets into Dagster from a live Airbyte instance, you will need to supply the Airbyte resource that we defined above in step 1. Here, the Airbyte instance is treated as the source of truth.

from dagster_airbyte import load_assets_from_airbyte_instance

# Use the airbyte_instance resource we defined in Step 1
airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance)

The load_assets_from_airbyte_instance function retrieves all of the connections you have defined in the Airbyte interface, creating asset definitions for each data stream. Each connection has an associated op which triggers a sync of that connection.

Loading Airbyte asset definitions from YAML config

(Deprecated)

#

To load Airbyte assets into Dagster from a set of YAML configuration files, specify the Octavia project directory, which contains the sources, destinations, and connections subfolders. This is the directory where you first ran octavia init. Here, the YAML files are treated as the source of truth for building Dagster assets.

from dagster_airbyte import load_assets_from_airbyte_project

airbyte_assets = load_assets_from_airbyte_project(
    project_dir="path/to/airbyte/project",
)

The load_assets_from_airbyte_project function parses the YAML metadata, generating a set of asset definitions which reflect each of the data streams synced by your connections. Each connection has an associated op which triggers a sync of that connection.

Adding a resource#

Assets loaded from Airbyte require an AirbyteResource, which defines how to connect and interact with your Airbyte instance.

We can add the Airbyte resource we configured above to our Airbyte assets by doing the following:

from dagster_airbyte import load_assets_from_airbyte_project

from dagster import with_resources

# Use the airbyte_instance resource we defined in Step 1
airbyte_assets = with_resources(
    [load_assets_from_airbyte_project(project_dir="path/to/airbyte/project")],
    {"airbyte": airbyte_instance},
)

Manually building Airbyte asset definitions#

Instead of having Dagster automatically create the asset defintions for your Airbyte instance, you can opt to individually build them. First, determine the connection IDs for each of the connections you would like to build assets for. The connection ID can be seen in the URL of the connection page when viewing the Airbyte UI.

Then, supply the connection ID and the list of tables which the connection creates in the destination to build_airbyte_assets:

from dagster_airbyte import build_airbyte_assets

airbyte_assets = build_airbyte_assets(
    connection_id="87b7fe85-a22c-420e-8d74-b30e7ede77df",
    destination_tables=["releases", "tags", "teams", "stargazers"],
)

Adding a resource#

Manually built Airbyte assets require an AirbyteResource, which defines how to connect and interact with your Airbyte instance.

We can add the Airbyte resource we configured above to our Airbyte assets by doing the following:

from dagster_airbyte import build_airbyte_assets, AirbyteResource

from dagster import with_resources

airbyte_instance = AirbyteResource(
    host="localhost",
    port="8000",
)
airbyte_assets = with_resources(
    build_airbyte_assets(
        connection_id="87b7fe85-a22c-420e-8d74-b30e7ede77df",
        destination_tables=["releases", "tags", "teams", "stargazers"],
    ),
    # Use the airbyte_instance resource we defined in Step 1
    {"airbyte": airbyte_instance},
)

In this case, we have an Airbyte connection that stores data in the stargazers table in our Snowflake warehouse. We specify the output I/O manager to tell downstream assets how to retrieve the data.

import json
from dagster import (
    AssetSelection,
    Definitions,
    asset,
    define_asset_job,
)
from dagster_airbyte import load_assets_from_airbyte_instance, AirbyteResource
from dagster_snowflake_pandas import SnowflakePandasIOManager
import pandas as pd

airbyte_instance = AirbyteResource(
    host="localhost",
    port="8000",
)

airbyte_assets = load_assets_from_airbyte_instance(
    airbyte_instance,
    io_manager_key="snowflake_io_manager",
)

@asset
def stargazers_file(stargazers: pd.DataFrame):
    with open("stargazers.json", "w", encoding="utf8") as f:
        f.write(json.dumps(stargazers.to_json(), indent=2))

# only run the airbyte syncs necessary to materialize stargazers_file
my_upstream_job = define_asset_job(
    "my_upstream_job",
    AssetSelection.assets(stargazers_file)
    .upstream()  # all upstream assets (in this case, just the stargazers Airbyte asset)
    .required_multi_asset_neighbors(),  # all Airbyte assets linked to the same connection
)

defs = Definitions(
    jobs=[my_upstream_job],
    assets=[airbyte_assets, stargazers_file],
    resources={"snowflake_io_manager": SnowflakePandasIOManager(...)},
)

In this case, we have an Airbyte connection that stores data in the stargazers table in our Snowflake warehouse. Since we are not using an I/O manager to fetch the data in downstream assets, we will use deps to define dependencies. Then within the downstream asset, we can fetch the data if necessary or launch other commands that work with data in external processes.

import json
from dagster import (
    AssetSelection,
    AssetKey,
    Definitions,
    asset,
    define_asset_job,
)
from dagster_airbyte import load_assets_from_airbyte_instance, AirbyteResource
from dagster_snowflake import SnowflakeResource

airbyte_instance = AirbyteResource(
    host="localhost",
    port="8000",
)

airbyte_assets = load_assets_from_airbyte_instance(
    airbyte_instance,
)

@asset(deps=[AssetKey("stargazers")])
def stargazers_file(snowflake: SnowflakeResource):
    with snowflake.get_connection() as conn:
        stargazers = conn.cursor.execute(
            "SELECT * FROM STARGAZERS"
        ).fetch_pandas_all()
    with open("stargazers.json", "w", encoding="utf8") as f:
        f.write(json.dumps(stargazers.to_json(), indent=2))

# only run the airbyte syncs necessary to materialize stargazers_file
my_upstream_job = define_asset_job(
    "my_upstream_job",
    AssetSelection.assets(stargazers_file)
    .upstream()  # all upstream assets (in this case, just the stargazers Airbyte asset)
    .required_multi_asset_neighbors(),  # all Airbyte assets linked to the same connection
)

defs = Definitions(
    jobs=[my_upstream_job],
    assets=[airbyte_assets, stargazers_file],
    resources={"snowflake": SnowflakeResource(...)},
)