[dagster-fivetran][docs] Migrate Fivetran docs to new doc site

docs/docs-beta/docs/integrations/libraries/fivetran.md

@@ -2,9 +2,9 @@
 layout: Integration
 status: published
 name: Fivetran
-title: Dagster & Fivetran
+title: Using Dagster with Fivetran
 sidebar_label: Fivetran
-excerpt: Orchestrate Fivetran connectors and schedule syncs with upstream or downstream dependencies.
+excerpt: Orchestrate Fivetran connectors syncs with upstream or downstream dependencies.
 date: 2022-11-07
 apireflink: https://docs.dagster.io/_apidocs/libraries/dagster-fivetran
 docslink: https://docs.dagster.io/integrations/fivetran
@@ -17,20 +17,63 @@ enables:
 tags: [dagster-supported, etl]
 ---
 
+This guide provides instructions for using Dagster with Fivetran using the `dagster-fivetran` library. Your Fivetran connector tables can be represented as assets in the Dagster asset graph, allowing you to track lineage and dependencies between Fivetran assets and data assets you are already modeling in Dagster. You can also use Dagster to orchestrate Fivetran connectors, allowing you to trigger syncs for these on a cadence or based on upstream data changes.
 
+## What you'll learn
 
-The Dagster-Fivetran integration enables you to orchestrate data ingestion as part of a larger pipeline. Programmatically interact with the Fivetran REST API to initiate syncs and monitor their progress.
+- How to represent Fivetran assets in the Dagster asset graph, including lineage to other Dagster assets.
+- How to customize asset definition metadata for these Fivetran assets.
+- How to materialize Fivetran connector tables from Dagster.
+- How to customize how Fivetran connector tables are materialized.
 
-### Installation
+<details>
+  <summary>Prerequisites</summary>
 
-```bash
-pip install dagster-fivetran
-```
+- The `dagster` and `dagster-fivetran` libraries installed in your environment
+- Familiarity with asset definitions and the Dagster asset graph
+- Familiarity with Dagster resources
+- Familiarity with Fivetran concepts, like connectors and connector tables
+- A Fivetran workspace
+- A Fivetran API key and API secret. For more information, see [Getting Started](https://fivetran.com/docs/rest-api/getting-started) in the Fivetran REST API documentation.
 
-### Example
+</details>
 
-<CodeExample filePath="integrations/fivetran.py" language="python" />
+## Represent Fivetran assets in the asset graph
+
+To load Fivetran assets into the Dagster asset graph, you must first construct a <PyObject module="dagster_fivetran" object="FivetranWorkspace" /> resource, which allows Dagster to communicate with your Fivetran workspace. You'll need to supply your account ID, API key and API secret. See [Getting Started](https://fivetran.com/docs/rest-api/getting-started) in the Fivetran REST API documentation for more information on how to create your API key and API secret.
+
+Dagster can automatically load all connector tables from your Fivetran workspace as asset specs. Call the <PyObject module="dagster_fivetran" method="load_fivetran_asset_specs" /> function, which returns list of <PyObject object="AssetSpec" />s representing your Fivetran assets. You can then include these asset specs in your <PyObject object="Definitions" /> object:
+
+<CodeExample filePath="integrations/fivetran/representing_fivetran_assets.py" language="python" />
+
+### Sync and materialize Fivetran assets
+
+You can use Dagster to sync Fivetran connectors and materialize Fivetran connector tables. You can use the <PyObject module="dagster_fivetran" method="build_fivetran_assets_definitions" /> factory to create all assets definitions for your Fivetran workspace.
+
+<CodeExample filePath="integrations/fivetran/sync_and_materialize_fivetran_assets.py" language="python" />
+
+### Customize the materialization of Fivetran assets
+
+If you want to customize the sync of your connectors, you can use the <PyObject module="dagster_fivetran" method="fivetran_assets" /> decorator to do so. This allows you to execute custom code before and after the call to the Fivetran sync.
+
+<CodeExample filePath="integrations/fivetran/customize_fivetran_asset_defs.py" language="python" />
+
+### Customize asset definition metadata for Fivetran assets
+
+By default, Dagster will generate asset specs for each Fivetran asset and populate default metadata. You can further customize asset properties by passing an instance of the custom <PyObject module="dagster_fivetran" object="DagsterFivetranTranslator" /> to the <PyObject module="dagster_fivetran" method="load_fivetran_asset_specs" /> function.
+
+<CodeExample filePath="integrations/fivetran/customize_fivetran_translator_asset_spec.py" language="python" />
+
+Note that `super()` is called in each of the overridden methods to generate the default asset spec. It is best practice to generate the default asset spec before customizing it.
+
+You can pass an instance of the custom <PyObject module="dagster_fivetran" object="DagsterFivetranTranslator" /> to the <PyObject module="dagster_fivetran" method="fivetran_assets" /> decorator or the <PyObject module="dagster_fivetran" method="build_fivetran_assets_definitions" /> factory.
+
+### Load Fivetran assets from multiple workspaces
+
+Definitions from multiple Fivetran workspaces can be combined by instantiating multiple <PyObject module="dagster_fivetran" object="FivetranWorkspace" /> resources and merging their specs. This lets you view all your Fivetran assets in a single asset graph:
+
+<CodeExample filePath="integrations/fivetran/multiple_fivetran_workspaces.py" language="python" />
 
 ### About Fivetran
 
-**Fivetran** ingests data from SaaS applications, databases, and servers. The data is stored and typically used for analytics.
+**Fivetran** ingests data from SaaS applications, databases, and servers. The data is stored and typically used for analytics.

examples/docs_beta_snippets/docs_beta_snippets/integrations/fivetran/customize_fivetran_asset_defs.py

@@ -0,0 +1,29 @@
+from dagster_fivetran import FivetranWorkspace, fivetran_assets
+
+import dagster as dg
+
+fivetran_workspace = FivetranWorkspace(
+    account_id=dg.EnvVar("FIVETRAN_ACCOUNT_ID"),
+    api_key=dg.EnvVar("FIVETRAN_API_KEY"),
+    api_secret=dg.EnvVar("FIVETRAN_API_SECRET"),
+)
+
+
+@fivetran_assets(
+    connector_id="fivetran_connector_id",
+    name="fivetran_connector_id",
+    group_name="fivetran_connector_id",
+    workspace=fivetran_workspace,
+)
+def fivetran_connector_assets(
+    context: dg.AssetExecutionContext, fivetran: FivetranWorkspace
+):
+    # Do something before the materialization...
+    yield from fivetran.sync_and_poll(context=context)
+    # Do something after the materialization...
+
+
+defs = dg.Definitions(
+    assets=[fivetran_connector_assets],
+    resources={"fivetran": fivetran_workspace},
+)

examples/docs_beta_snippets/docs_beta_snippets/integrations/fivetran/customize_fivetran_translator_asset_spec.py

@@ -0,0 +1,33 @@
+from dagster_fivetran import (
+    DagsterFivetranTranslator,
+    FivetranConnectorTableProps,
+    FivetranWorkspace,
+    load_fivetran_asset_specs,
+)
+
+import dagster as dg
+
+fivetran_workspace = FivetranWorkspace(
+    account_id=dg.EnvVar("FIVETRAN_ACCOUNT_ID"),
+    api_key=dg.EnvVar("FIVETRAN_API_KEY"),
+    api_secret=dg.EnvVar("FIVETRAN_API_SECRET"),
+)
+
+
+# A translator class lets us customize properties of the built
+# Fivetran assets, such as the owners or asset key
+class MyCustomFivetranTranslator(DagsterFivetranTranslator):
+    def get_asset_spec(self, props: FivetranConnectorTableProps) -> dg.AssetSpec:
+        # We create the default asset spec using super()
+        default_spec = super().get_asset_spec(props)
+        # We customize the metadata and asset key prefix for all assets
+        return default_spec.replace_attributes(
+            key=default_spec.key.with_prefix("prefix"),
+        ).merge_attributes(metadata={"custom": "metadata"})
+
+
+fivetran_specs = load_fivetran_asset_specs(
+    fivetran_workspace, dagster_fivetran_translator=MyCustomFivetranTranslator()
+)
+
+defs = dg.Definitions(assets=fivetran_specs, resources={"fivetran": fivetran_workspace})

examples/docs_beta_snippets/docs_beta_snippets/integrations/fivetran/multiple_fivetran_workspaces.py

@@ -0,0 +1,26 @@
+from dagster_fivetran import FivetranWorkspace, load_fivetran_asset_specs
+
+import dagster as dg
+
+sales_fivetran_workspace = FivetranWorkspace(
+    account_id=dg.EnvVar("FIVETRAN_SALES_ACCOUNT_ID"),
+    api_key=dg.EnvVar("FIVETRAN_SALES_API_KEY"),
+    api_secret=dg.EnvVar("FIVETRAN_SALES_API_SECRET"),
+)
+marketing_fivetran_workspace = FivetranWorkspace(
+    account_id=dg.EnvVar("FIVETRAN_MARKETING_ACCOUNT_ID"),
+    api_key=dg.EnvVar("FIVETRAN_MARKETING_API_KEY"),
+    api_secret=dg.EnvVar("FIVETRAN_MARKETING_API_SECRET"),
+)
+
+sales_fivetran_specs = load_fivetran_asset_specs(sales_fivetran_workspace)
+marketing_fivetran_specs = load_fivetran_asset_specs(marketing_fivetran_workspace)
+
+# Merge the specs into a single set of definitions
+defs = dg.Definitions(
+    assets=[*sales_fivetran_specs, *marketing_fivetran_specs],
+    resources={
+        "marketing_fivetran": marketing_fivetran_workspace,
+        "sales_fivetran": sales_fivetran_workspace,
+    },
+)

examples/docs_beta_snippets/docs_beta_snippets/integrations/fivetran/representing_fivetran_assets.py

@@ -0,0 +1,12 @@
+from dagster_fivetran import FivetranWorkspace, load_fivetran_asset_specs
+
+import dagster as dg
+
+fivetran_workspace = FivetranWorkspace(
+    account_id=dg.EnvVar("FIVETRAN_ACCOUNT_ID"),
+    api_key=dg.EnvVar("FIVETRAN_API_KEY"),
+    api_secret=dg.EnvVar("FIVETRAN_API_SECRET"),
+)
+
+fivetran_specs = load_fivetran_asset_specs(fivetran_workspace)
+defs = dg.Definitions(assets=fivetran_specs, resources={"fivetran": fivetran_workspace})

examples/docs_beta_snippets/docs_beta_snippets/integrations/fivetran/sync_and_materialize_fivetran_assets.py

@@ -0,0 +1,16 @@
+from dagster_fivetran import FivetranWorkspace, build_fivetran_assets_definitions
+
+import dagster as dg
+
+fivetran_workspace = FivetranWorkspace(
+    account_id=dg.EnvVar("FIVETRAN_ACCOUNT_ID"),
+    api_key=dg.EnvVar("FIVETRAN_API_KEY"),
+    api_secret=dg.EnvVar("FIVETRAN_API_SECRET"),
+)
+
+all_fivetran_assets = build_fivetran_assets_definitions(workspace=fivetran_workspace)
+
+defs = dg.Definitions(
+    assets=all_fivetran_assets,
+    resources={"fivetran": fivetran_workspace},
+)

examples/docs_beta_snippets/docs_beta_snippets_tests/test_integration_files_load.py

@@ -19,7 +19,11 @@
     f"{snippets_folder}/sdf.py",
     f"{snippets_folder}/airbyte.py",
     f"{snippets_folder}/dlt.py",
-    f"{snippets_folder}/fivetran.py",
+    f"{snippets_folder}/fivetran/customize_fivetran_asset_defs.py",
+    f"{snippets_folder}/fivetran/customize_fivetran_translator_asset_spec.py",
+    f"{snippets_folder}/fivetran/multiple_fivetran_workspaces.py",
+    f"{snippets_folder}/fivetran/representing_fivetran_assets.py",
+    f"{snippets_folder}/fivetran/sync_and_materialize_fivetran_assets.py",
     # FIXME: this breaks on py3.8 and seems related to the non-dagster dependencies
     f"{snippets_folder}/pandera.py",
 }