From fb316ed108c421b90ff942126e442fb2dc3680dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mayra=20Pena=28Pe=C3=B1a=29?= <97967943+mayrapena1324@users.noreply.github.com> Date: Wed, 11 Sep 2024 17:49:38 -0500 Subject: [PATCH] Create databricks doc page (#35) * Create databricks doc page * Fix reference to host and cluster id * Update ADF docs * schedule_interval -> schedule * Remove k8 reference * Alpha order sidebar * Update datahub docs * Fix Alpha order of Notifications * Change Data Set to dataset --------- Co-authored-by: Mayra Pena Co-authored-by: Noel Gomez --- docs/_sidebar.md | 64 +++++----- .../datacoves/folder-structure.md | 2 +- docs/getting-started/Admin/create-account.md | 2 +- .../developer/snowflake-extension.md | 2 +- .../airflow/assets/airflow_adf_connection.png | Bin 0 -> 363641 bytes .../assets/airflow_databricks_connection.png | Bin 0 -> 62498 bytes .../airflow/assets/databricks_compute.png | Bin 0 -> 38363 bytes .../airflow/assets/databricks_copyurl.png | Bin 0 -> 56413 bytes .../airflow/customize-worker-environment.md | 48 ++++--- .../airflow/dynamically-set-schedule.md | 4 +- docs/how-tos/airflow/external-python-dag.md | 2 +- .../how-tos/airflow/generate-dags-from-yml.md | 4 +- .../airflow/request-resources-on-workers.md | 35 +++-- docs/how-tos/airflow/run-adf-pipeline.md | 112 ++++++++++++++++ docs/how-tos/airflow/run-airbyte-sync-jobs.md | 2 +- .../airflow/run-databricks-notebook.md | 120 ++++++++++++++++++ docs/how-tos/airflow/run-dbt.md | 4 +- .../how-tos/airflow/run-fivetran-sync-jobs.md | 4 +- docs/how-tos/airflow/send-emails.md | 4 +- .../airflow/send-ms-teams-notifications.md | 4 +- .../airflow/send-slack-notifications.md | 32 +---- docs/how-tos/airflow/sync-database.md | 2 +- .../airflow/use-variables-and-connections.md | 4 +- docs/how-tos/datahub/README.md | 2 +- docs/how-tos/datahub/how_to_datahub_cli.md | 42 ++++-- 25 files changed, 356 insertions(+), 139 deletions(-) create mode 100644 docs/how-tos/airflow/assets/airflow_adf_connection.png create mode 100644 docs/how-tos/airflow/assets/airflow_databricks_connection.png create mode 100644 docs/how-tos/airflow/assets/databricks_compute.png create mode 100644 docs/how-tos/airflow/assets/databricks_copyurl.png create mode 100644 docs/how-tos/airflow/run-adf-pipeline.md create mode 100644 docs/how-tos/airflow/run-databricks-notebook.md diff --git a/docs/_sidebar.md b/docs/_sidebar.md index 4b3388a..8361a9a 100644 --- a/docs/_sidebar.md +++ b/docs/_sidebar.md @@ -13,31 +13,24 @@ - **Diving Deeper** - [How to](/how-tos/) - [Airflow](/how-tos/airflow/) - - [Initial setup](/how-tos/airflow/initial-setup.md) - - [Run dbt](/how-tos/airflow/run-dbt.md) - - [Generate DAGs from yml](/how-tos/airflow/generate-dags-from-yml.md) - - [Calling External Python Scripts](/how-tos/airflow/external-python-dag.md) - - [Use Variables and Connections](/how-tos/airflow/use-variables-and-connections.md) - - [Dynamically Set Schedule](/how-tos/airflow/dynamically-set-schedule.md) - - [Run Airbyte sync jobs](/how-tos/airflow/run-airbyte-sync-jobs.md) - - [Run Fivetran sync jobs](/how-tos/airflow/run-fivetran-sync-jobs.md) - - [Add Dag Documentation](/how-tos/airflow/create-dag-level-docs.md) - - [Send Emails](/how-tos/airflow/send-emails.md) - - [Send Microsoft Teams notifications](/how-tos/airflow/send-ms-teams-notifications.md) - - [Send Slack notifications](/how-tos/airflow/send-slack-notifications.md) - - [Get Current Branch Name from a DAG Task](/how-tos/airflow/get-current-branch-name.md) - - [Custom Worker Environment](/how-tos/airflow/customize-worker-environment.md) - - [Request Memory and CPU](/how-tos/airflow/request-resources-on-workers.md) - - [Sync Airflow database](/how-tos/airflow/sync-database.md) - - [VS Code](/how-tos/vscode/) - - [Initial Configuration](/how-tos/vscode/initial.md) - - [BigQuery](/how-tos/vscode/bigquery_setup.md) - - [Databricks](/how-tos/vscode/databricks_setup.md) - - [Redshift](/how-tos/vscode/redshift_setup.md) - - [Snowflake](/how-tos/vscode/snowflake_setup.md) - - [Override VS Code settings](/how-tos/vscode/override.md) - - [Reset User Env](/how-tos/vscode/reset-user-env.md) - - [Reset Git](how-tos/vscode/reset-git.md) + - [Airflow - Initial setup](/how-tos/airflow/initial-setup.md) + - [Airflow - Sync Airflow database](/how-tos/airflow/sync-database.md) + - [DAGs - Add Dag Documentation](/how-tos/airflow/create-dag-level-docs.md) + - [DAGs - Calling External Python Scripts](/how-tos/airflow/external-python-dag.md) + - [DAGs - Dynamically Set Schedule](/how-tos/airflow/dynamically-set-schedule.md) + - [DAGs - Generate DAGs from yml](/how-tos/airflow/generate-dags-from-yml.md) + - [DAGs - Run ADF Pipelines](/how-tos/airflow/run-adf-pipeline.md) + - [DAGs - Run Airbyte sync jobs](/how-tos/airflow/run-airbyte-sync-jobs.md) + - [DAGs - Run dbt](/how-tos/airflow/run-dbt.md) + - [DAGs - Run Databricks Notebooks](/how-tos/airflow/run-databricks-notebook.md) + - [DAGs - Run Fivetran sync jobs](/how-tos/airflow/run-fivetran-sync-jobs.md) + - [DAGs - Use Variables and Connections](/how-tos/airflow/use-variables-and-connections.md) + - [Git - Get Current Branch Name from a DAG Task](/how-tos/airflow/get-current-branch-name.md) + - [Notifications - Send Emails](/how-tos/airflow/send-emails.md) + - [Notifications - Send Microsoft Teams notifications](/how-tos/airflow/send-ms-teams-notifications.md) + - [Notifications - Send Slack notifications](/how-tos/airflow/send-slack-notifications.md) + - [Worker - Custom Worker Environment](/how-tos/airflow/customize-worker-environment.md) + - [Worker - Request Memory and CPU](/how-tos/airflow/request-resources-on-workers.md) - [Datacoves](/how-tos/datacoves/) - [Configure Connection Templates](/how-tos/datacoves/how_to_connection_template.md) - [Configure Environments](/how-tos/datacoves/how_to_environments.md) @@ -48,9 +41,6 @@ - [Configure Service Connections](/how-tos/datacoves/how_to_service_connections.md) - [Manage Users](/how-tos/datacoves/how_to_manage_users.md) - [Update Repository](/getting-started/Admin/configure-repository.md) - - [Superset](/how-tos/superset/) - - [Add a Database](/how-tos/superset/how_to_database.md) - - [Add a Data Set](/how-tos/superset/how_to_data_set.md) - [Datahub](/how-tos/datahub/) - [Manage datahub using CLI](/how-tos/datahub/how_to_datahub_cli.md) - [DataOps](/how-tos/dataops/) @@ -59,6 +49,18 @@ - [SSH Keys configuration](/how-tos/git/ssh-keys) - [Snowflake](/how-tos/snowflake/) - [Warehouses, Schemas and Roles](/how-tos/snowflake/warehouses-schemas-roles) + - [Superset](/how-tos/superset/) + - [Add a Database](/how-tos/superset/how_to_database.md) + - [Add a Dataset](/how-tos/superset/how_to_data_set.md) + - [VS Code](/how-tos/vscode/) + - [Initial Configuration](/how-tos/vscode/initial.md) + - [BigQuery](/how-tos/vscode/bigquery_setup.md) + - [Databricks](/how-tos/vscode/databricks_setup.md) + - [Redshift](/how-tos/vscode/redshift_setup.md) + - [Snowflake](/how-tos/vscode/snowflake_setup.md) + - [Override VS Code settings](/how-tos/vscode/override.md) + - [Reset User Env](/how-tos/vscode/reset-user-env.md) + - [Reset Git](how-tos/vscode/reset-git.md) - [Explanation](/explanation/) - [Best Practices](/explanation/best-practices/) - [Datacoves](/explanation/best-practices/datacoves/) @@ -71,7 +73,6 @@ - [Snowflake](/explanation/best-practices/snowflake/) - [Security Model](/explanation/best-practices/snowflake/security-model) - [GDPR and Time-Travel](/explanation/best-practices/snowflake/time-travel) -- [Tutorials](/tutorials/) - [Reference](/reference/) - [Administration Menu](reference/admin-menu/) - [Account Settings & Billing](/reference/admin-menu/settings_billing.md) @@ -89,11 +90,12 @@ - [Datacoves Operators](/reference/airflow/datacoves-operator.md) - [Datacoves](/reference/datacoves/) - [VPC Deployment](/reference/datacoves/vpc-deployment.md) - - [VS Code](/reference/vscode/) - - [Datacoves Environment Variables](/reference/vscode/datacoves-env-vars.md) - [Metrics & Logs](/reference/metrics-and-logs/) - [Grafana](/reference/metrics-and-logs/grafana.md) - [Security](/reference/security/) + - [VS Code](/reference/vscode/) + - [Datacoves Environment Variables](/reference/vscode/datacoves-env-vars.md) +- [Tutorials](/tutorials/) - **Platform** - [Status Tracker](https://datacoves.statuspage.io/) - [SLA](sla.md) diff --git a/docs/explanation/best-practices/datacoves/folder-structure.md b/docs/explanation/best-practices/datacoves/folder-structure.md index c85293f..be3cd92 100644 --- a/docs/explanation/best-practices/datacoves/folder-structure.md +++ b/docs/explanation/best-practices/datacoves/folder-structure.md @@ -65,4 +65,4 @@ The `visualization` folder is used to place configs related to superset or other The `visualization/streamlit` folder is used for Streamlit apps. This folder is only needed if using Streamlit. ### .vscode/settings.json -The `.vscode/settings.json` folder is used for customized settings in order to override the default workspace settings. This file can contain secrets so be sure to add it to the `.gitignore` to avoid version control. See our [How to Override default VS Code settings](how-tos/vscode/override.md) for more info \ No newline at end of file +The `.vscode/settings.json` folder is used for customized settings in order to override the default workspace settings. This file can contain secrets so be sure to add it to the `.gitignore` to avoid version control. See our [How to Override default VS sCode settings](how-tos/vscode/override.md) for more info \ No newline at end of file diff --git a/docs/getting-started/Admin/create-account.md b/docs/getting-started/Admin/create-account.md index f12e7f4..b239899 100644 --- a/docs/getting-started/Admin/create-account.md +++ b/docs/getting-started/Admin/create-account.md @@ -42,5 +42,5 @@ To ensure a smooth call, please have the answers to the following questions read - What do you want to call your account? (This is usually the company name) - What do you want to call your project? (This can be something like Marketing DW, Finance 360, etc) - Do you currently have a CI/CD process and associated script like GitHub Actions workflow? If not, do you plan on creating a CI/CD process? -- Do you need any specific python library on Airflow or VS code? (outside the standard dbt related items) +- Do you need any specific python library on Airflow or VS Code? (outside the standard dbt related items) diff --git a/docs/getting-started/developer/snowflake-extension.md b/docs/getting-started/developer/snowflake-extension.md index a554f50..2324e07 100644 --- a/docs/getting-started/developer/snowflake-extension.md +++ b/docs/getting-started/developer/snowflake-extension.md @@ -8,7 +8,7 @@ - Autocomplete - Running Queries -For more information, please see the **[Snowflake VSCode Extension Docs](https://docs.snowflake.com/en/user-guide/vscode-ext)** +For more information, please see the **[Snowflake VS Code Extension Docs](https://docs.snowflake.com/en/user-guide/vscode-ext)**
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a/docs/how-tos/airflow/customize-worker-environment.md +++ b/docs/how-tos/airflow/customize-worker-environment.md @@ -24,10 +24,8 @@ TRANSFORM_CONFIG = { spec=k8s.V1PodSpec( containers=[ k8s.V1Container( - name="transform", - # Replace with your image repo and tag + name="base", image=":", - bash_command="echo SUCCESS!", ) ] ) @@ -46,43 +44,41 @@ TRANSFORM_CONFIG = { schedule_interval="0 0 1 */12 *", tags=["version_2"], catchup=False, - yaml_sample_dag={ - "schedule_interval": "0 0 1 */12 *", - "tags": ["version_4"], - "catchup": False, - "default_args": { - "start_date": datetime.datetime(2023, 1, 1, 0, 0), - "owner": "airflow", - "email": "some_user@exanple.com", - "email_on_failure": True, - }, - }, ) -def custommize_worker_dag(): +def yaml_teams_dag(): transform = DatacovesBashOperator( - task_id="transform", executor_config=TRANSFORM_CONFIG + task_id="transform", + bash_command="echo SUCCESS!", + executor_config=TRANSFORM_CONFIG, ) -dag = customize_worker_dag() +dag = yaml_teams_dag() ``` ### YAML version In the yml dag you can configure the image. ```yaml -... - - # DAG Tasks - nodes: - ... - transform: +description: "Sample DAG with custom image" +schedule_interval: "0 0 1 */12 *" +tags: + - version_2 +default_args: + start_date: 2023-01-01 + owner: Noel Gomez + email: gomezn@example.com + email_on_failure: true +catchup: false + +# DAG Tasks +nodes: + transform: operator: operators.datacoves.bash.DatacovesBashOperator type: task config: # Replace with your custom docker image : image: : - - bash_command: "echo SUCCESS!" - ... + + bash_command: "echo SUCCESS!" ``` diff --git a/docs/how-tos/airflow/dynamically-set-schedule.md b/docs/how-tos/airflow/dynamically-set-schedule.md index 257bc4a..8196ad8 100644 --- a/docs/how-tos/airflow/dynamically-set-schedule.md +++ b/docs/how-tos/airflow/dynamically-set-schedule.md @@ -44,7 +44,7 @@ def get_schedule(default_input: Union[str, None]) -> Union[str, None]: ``` **Step 3:** In your DAG, import the `get_schedule` function using `from orchestrate.python_scripts.get_schedule import get_schedule` and pass in your desired schedule. -ie) If your desired schedule is `'0 1 * * *'` then you will set `schedule_interval=get_schedule('0 1 * * *')` as seen in the example below. +ie) If your desired schedule is `'0 1 * * *'` then you will set `schedule=get_schedule('0 1 * * *')` as seen in the example below. ```python from airflow.decorators import dag from operators.datacoves.bash import DatacovesBashOperator @@ -66,7 +66,7 @@ from orchestrate.python_scripts.get_schedule import get_schedule # This is a regular CRON schedule. Helpful resources # https://cron-ai.vercel.app/ # https://crontab.guru/ - schedule_interval=get_schedule('0 1 * * *'), # Replace with desired schedule + schedule=get_schedule('0 1 * * *'), # Replace with desired schedule ) def datacoves_sample_dag(): # Calling dbt commands diff --git a/docs/how-tos/airflow/external-python-dag.md b/docs/how-tos/airflow/external-python-dag.md index 6e521e1..857327b 100644 --- a/docs/how-tos/airflow/external-python-dag.md +++ b/docs/how-tos/airflow/external-python-dag.md @@ -55,7 +55,7 @@ DATACOVES_VIRTUAL_ENV = "/opt/datacoves/virtualenvs/main/bin/activate" # This is a regular CRON schedule. Helpful resources # https://cron-ai.vercel.app/ # https://crontab.guru/ - schedule_interval="0 0 1 */12 *", + schedule="0 0 1 */12 *", ) def datacoves_sample_dag(): diff --git a/docs/how-tos/airflow/generate-dags-from-yml.md b/docs/how-tos/airflow/generate-dags-from-yml.md index 845a2d1..4faa698 100644 --- a/docs/how-tos/airflow/generate-dags-from-yml.md +++ b/docs/how-tos/airflow/generate-dags-from-yml.md @@ -45,7 +45,7 @@ Let's create our first DAG using YAML. ```yml description: "Sample DAG for dbt build" -schedule_interval: "0 0 1 */12 *" +schedule: "0 0 1 */12 *" tags: - version_2 default_args: @@ -127,7 +127,7 @@ from operators.datacoves.dbt import DatacovesDbtOperator "email_on_failure": True, }, description="Sample DAG for dbt build", - schedule_interval="0 0 1 */12 *", + schedule="0 0 1 */12 *", tags=["version_2"], catchup=False, ) diff --git a/docs/how-tos/airflow/request-resources-on-workers.md b/docs/how-tos/airflow/request-resources-on-workers.md index 9e75d33..f0fa141 100644 --- a/docs/how-tos/airflow/request-resources-on-workers.md +++ b/docs/how-tos/airflow/request-resources-on-workers.md @@ -12,11 +12,11 @@ In the following example, we're requesting a minimum of 8Gb of memory and 1000m ```python import datetime - from airflow.decorators import dag from kubernetes.client import models as k8s from operators.datacoves.bash import DatacovesBashOperator +# Configuration for Kubernetes Pod Override with Resource Requests TRANSFORM_CONFIG = { "pod_override": k8s.V1Pod( spec=k8s.V1PodSpec( @@ -32,7 +32,6 @@ TRANSFORM_CONFIG = { ), } - @dag( default_args={ "start_date": datetime.datetime(2023, 1, 1, 0, 0), @@ -41,27 +40,16 @@ TRANSFORM_CONFIG = { "email_on_failure": True, }, description="Sample DAG with custom resources", - schedule_interval="0 0 1 */12 *", + schedule="0 0 1 */12 *", tags=["version_2"], catchup=False, - yaml_sample_dag={ - "schedule_interval": "0 0 1 */12 *", - "tags": ["version_4"], - "catchup": False, - "default_args": { - "start_date": datetime.datetime(2023, 1, 1, 0, 0), - "owner": "airflow", - "email": "some_user@exanple.com", - "email_on_failure": True, - }, - }, ) def request_resources_dag(): - transform = DatacovesBashOperator( - task_id="transform", executor_config=TRANSFORM_CONFIG + transform_task = DatacovesBashOperator( + task_id="transform", + executor_config=TRANSFORM_CONFIG ) - dag = request_resources_dag() ``` @@ -69,9 +57,19 @@ dag = request_resources_dag() In the yml DAG you can configure the memory and cpu resources. ```yaml +description: "Sample DAG with custom resources" +schedule_interval: "0 0 1 */12 *" +tags: + - version_2 +default_args: + start_date: 2023-01-01 + owner: Noel Gomez + email: gomezn@example.com + email_on_failure: true +catchup: false + # DAG Tasks nodes: -... transform: operator: operators.datacoves.bash.DatacovesBashOperator type: task @@ -79,5 +77,4 @@ nodes: resources: memory: 8Gi cpu: 1000m -... ``` diff --git a/docs/how-tos/airflow/run-adf-pipeline.md b/docs/how-tos/airflow/run-adf-pipeline.md new file mode 100644 index 0000000..dffa378 --- /dev/null +++ b/docs/how-tos/airflow/run-adf-pipeline.md @@ -0,0 +1,112 @@ +# Use Microsoft Azure Data Factory Operators + +You can use Airflow in Datacoves to trigger a Microsoft Azure Data Factory pipeline. This guide will walk you through the configuration process. + +## Prerequisites + +- You will need to set up a [Microsoft Entra Application](https://learn.microsoft.com/en-us/entra/identity-platform/howto-create-service-principal-portal). +- Assign the `Data Factory Contributor` role to your Microsoft Entra Application. You can do this by heading into Resource Groups and then following [these instructions](https://learn.microsoft.com/en-us/entra/identity-platform/howto-create-service-principal-portal#assign-a-role-to-the-application). +- Collect the following values from your ADF account, more information on where to find these items in the next section: + - `DATA_FACTORY_NAME` + - `RESOURCE_GROUP_NAME` + - `SUBSCRIPTION_ID` + - `APPLICATION_CLIENT_ID` + - `TENANT_ID` + - `CLIENT_SECRET` + +### How to get the ADF information + +**Step 1:** Login to your Microsoft Azure console and navigate to the [Data Factories service](https://portal.azure.com/#view/HubsExtension/BrowseResource/resourceType/Microsoft.DataFactory%2FdataFactories). + +**Step 2:** Copy the`DATA_FACTORY_NAME` for the factory which holds your data pipeline. + +**Step 3:** Open the factory and copy the `RESOURCE_GROUP_NAME`, and the `SUBSCRIPTION_ID` from the Overview tab + +**Step 4:** Navigate to the Microsoft Entra ID service, click on number next to *Applicaitons* on the Overview tab. Next click the *All Applications* tab, open the application or register a new application then open it and copy the `APPLICATION_CLIENT_ID` and Directory`TENANT_ID`. + +**Step 5:** Click on *Certificates and Secrets* and generate a new secret for your Microsoft Entra Application and copy the *Value*. This is the `CLIENT_SECRET`. + +## Create a Microsoft Azure Data Factory Connection in Airflow + +**Step 1:** In Datacoves, a user with the `securityadmin` role must go to the `Airflow Admin -> Connection` menu. + + ![Airflow Connection](assets/admin-connections.png) + +**Step 2:** Create a new connection using the following details. + +- **Connection Id:** `azure_data_factory_default` <- this name will be used in the Airflow DAG and is the default name used by the ADF Operator + +- **Connection Type:** `Azure Data Factory` + +- **Client ID:** Your `APPLICATION_CLIENT_ID` + +- **Secret:** Your `CLIENT_SECRET` + +- **Tenant ID:** Your `TENANT_ID` + +- **Factory Name**: Your `DATA_FACTORY_NAME` + +- **Resource Group Name**: Your `RESOURCE_GROUP_NAME` + +- **Subscription ID:** Your `SUBSCRIPTION_ID` + +>[!NOTE] Replace the values in the screenshot below with the actual values found above. + +![adf connection](assets/airflow_adf_connection.png) + +## Example DAG +>[!NOTE] You will need to update the `pipeline_name`, `resource_group_name`, and `factory_name` arguments below with the correct names. + +Once you have configured your Databricks connection and variables, you are ready to create your DAG. Head into the `Transform` tab to begin writing your DAG inside `orchestrate/dags`. + +```python + +"""Example Airflow Azure Data Factory DAG.""" + +from datetime import datetime +from airflow.decorators import dag, task_group +from airflow.providers.microsoft.azure.operators.data_factory import AzureDataFactoryRunPipelineOperator +from airflow.providers.microsoft.azure.sensors.data_factory import AzureDataFactoryPipelineRunStatusSensor + +@dag( + schedule="@daily", + start_date=datetime(2024, 1, 1), + tags=["version_1"], + catchup=False, + default_args={ + "azure_data_factory_conn_id": "azure_data_factory_default", + "factory_name": "your-factory-name", + "resource_group_name": "your-resource-name", + }, +) +def adf_example_run(): + """Run an Azure Data Factory pipeline with async status checking.""" + + @task_group(group_id="adf_pipeline_group", tooltip="ADF Pipeline Group") + def adf_pipeline_tasks(): + run_pipeline = AzureDataFactoryRunPipelineOperator( + task_id="run_pipeline", + pipeline_name="myTestPipeline", # Rename to your Pipeline name + parameters={"myParam": "value"}, + wait_for_termination=False, + ) + + # Deferrable sensor for async pipeline status checking + pipeline_run_async_sensor = AzureDataFactoryPipelineRunStatusSensor( + task_id="pipeline_run_async_sensor", + run_id=run_pipeline.output["run_id"], + deferrable=True, + ) + + run_pipeline >> pipeline_run_async_sensor + + adf_pipeline_group = adf_pipeline_tasks() + +DAG = adf_example_run() +``` + +## Understanding the Airflow DAG + +- Understand how to [authenticate with ADF](https://airflow.apache.org/docs/apache-airflow-providers-microsoft-azure/stable/connections/adf.html). +- The DAG makes use of the [AzureDataFactoryRunPipelineOperator](https://airflow.apache.org/docs/apache-airflow-providers-microsoft-azure/stable/operators/adf_run_pipeline.html) to run an Azure Data Factory pipeline. It also shows how it’s possible to pass parameters that can be used in the pipeline. + \ No newline at end of file diff --git a/docs/how-tos/airflow/run-airbyte-sync-jobs.md b/docs/how-tos/airflow/run-airbyte-sync-jobs.md index 4301a27..4756c88 100644 --- a/docs/how-tos/airflow/run-airbyte-sync-jobs.md +++ b/docs/how-tos/airflow/run-airbyte-sync-jobs.md @@ -88,7 +88,7 @@ Now you are ready to write out your DAG using yml. In the following example DAG, ```yml description: "Loan Run" -schedule_interval: "0 0 1 */12 *" +schedule: "0 0 1 */12 *" tags: - version_1 default_args: diff --git a/docs/how-tos/airflow/run-databricks-notebook.md b/docs/how-tos/airflow/run-databricks-notebook.md new file mode 100644 index 0000000..82d3487 --- /dev/null +++ b/docs/how-tos/airflow/run-databricks-notebook.md @@ -0,0 +1,120 @@ +# Run Databricks Notebooks + +You can use Airflow in Datacoves to trigger a Databricks notebook. This guide will walk you through the configuration process. + +## Prerequisites + +- **Databricks Host:** This is the URL of your Databricks cluster. It typically looks like `https://.databricks.com`. +- **Databricks Cluster ID:** This is the identifier of the cluster you want to use to run your notebook. +- **Databricks Token:** If you do not have admin privileges, work with an admin to get the token. Follow the [Databricks documentation here](https://docs.databricks.com/en/dev-tools/auth/pat.html). +- **Databricks Notebook Repo Path:** This is the full path to the notebook you want to trigger from Airflow. We recommend using the Git Notebook feature in Databricks. This notebook is usually located in a Repos directory in Databricks. + +### How to get Databricks Host and Databricks Cluster ID + +**Step 1:** Sign into your Databricks account. + +**Step 2:** Navigate to compute. + +![databricks compute](assets/databricks_compute.png) + +**Step 3:** Click on your desired cluster. + +**Step 4:** Scroll to `Advanced Options` and `JDBC/ODBC`. Copy the value under `Server Hostname`. The host value will look something like this: `.databricks.com`. + +**Step 5:** Scroll to `Tags` and expand `Automatically added tags`. Your `ClusterId` should look something like this: `0123-5678910-abcdefghijk`. + +### How to get Databricks Token + +If you do not have admin privileges, work with an admin to get the token. Follow the [Databricks documentation here](https://docs.databricks.com/en/dev-tools/auth/pat.html). + +### How to get Databricks Notebook Path + +**Step 1:** Navigate to your notebook in the Databricks user interface. + +**Step 2:** To the right of the notebook name, there will be three dots. Click on this and select the option to copy the full path to your clipboard. + +![copy url](assets/databricks_copyurl.png) + +## Handling Databricks Variables in Airflow + +It is best practice to use Airflow variables for values that may need to change in your Airflow DAG. This allows for easy updates without redeployment of your Airflow code. +> [!NOTE] It is possible to hardcode these two variables in your DAG if you don’t see them needing to be changed. +- **DATABRICKS_CLUSTER_ID**: Your databricks Cluster ID +- **MY_NOTEBOOK_REPO_PATH**: This should be a meaningful name as you may have many notebooks you wish to trigger eg) INSERT_INTO_RAW_REPO_PATH + + +**Step 1:** A user with Airflow admin privileges must go to the Airflow `Admin -> Variables` menu and add the variables and their values. + + +## Create a Databricks Connection in Airflow + +**Step 1:** A user with Airflow admin privileges must go to the `Airflow Admin -> Connection` menu. + +![admin connection](assets/admin-connections.png) + +**Step 2:** Create a new connection using the following details: + +- **Connection Id:** `databricks_default` <- this name will be used in your DAG +- **Connection Type:** `Databricks` +- **Host:** Your Databricks host. E.g. `https://.databricks.com` +- **Password:** Enter your `Databricks Token` + +![Databricks Connection](assets/airflow_databricks_connection.png) + +**Step 3:** Click `Save` + +## Example DAG + +Once you have configured your Databricks connection and variables, you are ready to create your DAG. Head into the `Transform` tab to begin writing your DAG inside the dags folder, e.g. `orchestrate/dags`. + +### Git Notebook as the source (Recommended) + +We recommend using a git as the source to leverage version control when developing notebooks. Be aware that if changes are made in the databricks tracked branch (`GIT_BRANCH`), they will be executed in Airflow regardless if the changes are committed into Git. The best practice is to have users develop on feature branches and then merge to main. + +```python +import os +from datetime import datetime +from airflow.models import Variable +from airflow.decorators import dag +from airflow.providers.databricks.operators.databricks import DatabricksSubmitRunDeferrableOperator + +DATABRICKS_CLUSTER_ID = Variable.get("DATABRICKS_CLUSTER_ID") +MY_NOTEBOOK_REPO_PATH = "/PATH/TO/MY/NOTEBOOK" +GIT_BRANCH = "main" # Specify the branch you want to use + +@dag( + schedule="@daily", + start_date=datetime(2024, 1, 1), + tags=["version_1"], + catchup=False +) +def databricks_example_run(): + + notebook_task_params = { + "task_key": "unique-task-key", + "notebook_task": { + "notebook_path": MY_NOTEBOOK_REPO_PATH, + "base_parameters": { + "branch": GIT_BRANCH # Specify the branch in variable + } + }, + "source": "GIT", + "existing_cluster_id": DATABRICKS_CLUSTER_ID, + "run_name": "databricks_workbook_run", # Update with a unique name + } + + DatabricksSubmitRunDeferrableOperator( + task_id="notebook_task", # Rename with appropriate name + json=notebook_task_params, + databricks_conn_id="databricks_default" # Must match databricks connection id set above + ) + +dag = databricks_example_run() +``` +## Understanding the Airflow DAG + +- The DAG makes use of the [`DatabricksSubmitRunDeferrableOperator`](https://airflow.apache.org/docs/apache-airflow-providers-databricks/stable/operators/submit_run.html) which uses the [jobs/runs/submit](https://docs.databricks.com/api/workspace/jobs/submit) endpoint of the Databricks API. You can see the full list of options available by looking at the previous two links. + +- We’re passing a [Notebook task object](https://docs.databricks.com/api/workspace/jobs/submit#notebook_task) with the source set to `GIT`, meaning the notebook will be retrieved from Databricks Repos, which is synced with a version-controlled Git repository. Alternatively, you can set the source to `WORKSPACE` to pull the notebook code directly from the local Databricks workspace. Using `GIT` is generally more reliable for production environments because it ensures that the notebook code is managed through a version-controlled system, providing consistency and traceability. + +- And lastly, we have customized the `run_name`. In a non-example DAG, you would want this to be unique so you can better identify the runs in Airflow and Databricks. diff --git a/docs/how-tos/airflow/run-dbt.md b/docs/how-tos/airflow/run-dbt.md index 978bc8d..e8e5729 100644 --- a/docs/how-tos/airflow/run-dbt.md +++ b/docs/how-tos/airflow/run-dbt.md @@ -42,7 +42,7 @@ from operators.datacoves.dbt import DatacovesDbtOperator "email_on_failure": True, }, description="Sample DAG for dbt run", - schedule_interval="0 0 1 */12 *", # Replace with your desired schedule + schedule="0 0 1 */12 *", # Replace with your desired schedule tags=["version_2"], catchup=False, ) @@ -68,7 +68,7 @@ The name of the file will used as the DAG name. ```yaml description: "Sample DAG for dbt run" -schedule_interval: "0 0 1 */12 *" +schedule: "0 0 1 */12 *" tags: - version_2 default_args: diff --git a/docs/how-tos/airflow/run-fivetran-sync-jobs.md b/docs/how-tos/airflow/run-fivetran-sync-jobs.md index 3d91186..8f38e7c 100644 --- a/docs/how-tos/airflow/run-fivetran-sync-jobs.md +++ b/docs/how-tos/airflow/run-fivetran-sync-jobs.md @@ -69,7 +69,7 @@ You will need to define two operators: `fivetran_provider.operators.fivetran.Fiv ```yaml description: "Loan Run" -schedule_interval: "0 0 1 */12 *" +schedule: "0 0 1 */12 *" tags: - version_1 default_args: @@ -124,7 +124,7 @@ from operators.datacoves.dbt import DatacovesDbtOperator @dag( default_args={"start_date": "2024-01"}, description="Loan Run", - schedule_interval="0 0 1 */12 *", + schedule="0 0 1 */12 *", tags=["version_1"], catchup=False, ) diff --git a/docs/how-tos/airflow/send-emails.md b/docs/how-tos/airflow/send-emails.md index 4dfe365..81cff45 100644 --- a/docs/how-tos/airflow/send-emails.md +++ b/docs/how-tos/airflow/send-emails.md @@ -86,7 +86,7 @@ from operators.datacoves.dbt import DatacovesDbtOperator "email_on_failure": True, }, description="Sample DAG for dbt build", - schedule_interval="0 0 1 */12 *", + schedule="0 0 1 */12 *", tags=["version_1"], catchup=False, ) @@ -104,7 +104,7 @@ dag = dbt_run() ```yaml description: "Sample DAG for dbt run" -schedule_interval: "0 0 1 */12 *" +schedule: "0 0 1 */12 *" tags: - version_1 default_args: diff --git a/docs/how-tos/airflow/send-ms-teams-notifications.md b/docs/how-tos/airflow/send-ms-teams-notifications.md index 8ea1df6..feecdd6 100644 --- a/docs/how-tos/airflow/send-ms-teams-notifications.md +++ b/docs/how-tos/airflow/send-ms-teams-notifications.md @@ -93,7 +93,7 @@ from notifiers.datacoves.ms_teams import MSTeamsNotifier "email_on_failure": True, }, description="Sample DAG with MS Teams notification", - schedule_interval="0 0 1 */12 *", + schedule="0 0 1 */12 *", tags=["version_2", "ms_teams_notification", "blue_green"], catchup=False, on_success_callback=MSTeamsNotifier( @@ -115,7 +115,7 @@ dag = yaml_teams_dag() ```yaml description: "Sample DAG with MS Teams notification" -schedule_interval: "0 0 1 */12 *" +schedule: "0 0 1 */12 *" tags: - version_2 - ms_teams_notification diff --git a/docs/how-tos/airflow/send-slack-notifications.md b/docs/how-tos/airflow/send-slack-notifications.md index b8022c9..b3ec85c 100644 --- a/docs/how-tos/airflow/send-slack-notifications.md +++ b/docs/how-tos/airflow/send-slack-notifications.md @@ -97,25 +97,9 @@ To send Slack notifications, in the Airflow DAG we need to import the appropriat import datetime from airflow.decorators import dag -from kubernetes.client import models as k8s from operators.datacoves.dbt import DatacovesDbtOperator from airflow.providers.slack.notifications.slack_webhook import send_slack_webhook_notification -TRANSFORM_CONFIG = { - "pod_override": k8s.V1Pod( - spec=k8s.V1PodSpec( - containers=[ - k8s.V1Container( - name="transform", - image="datacoves/airflow-pandas:latest", - resources=k8s.V1ResourceRequirements( - requests={"memory": "8Gi", "cpu": "1000m"} - ), - ) - ] - ) - ), -} run_inform_success = send_slack_webhook_notification( slack_webhook_conn_id="SLACK_NOTIFICATIONS", # Slack integration name slug -- double check in Datacoves integrations' admin @@ -133,9 +117,9 @@ run_inform_failure = send_slack_webhook_notification( "email": "gomezn@example.com", "email_on_failure": True, }, - description="Sample DAG with Slack notification, custom image, and resource requests", - schedule_interval="0 0 1 */12 *", - tags=["version_2", "slack_notification", "blue_green"], + description="Sample DAG with Slack notification", + schedule="0 0 1 */12 *", + tags=["version_2"], catchup=False, on_success_callback=[run_inform_success], on_failure_callback=[run_inform_failure], @@ -144,7 +128,6 @@ def yaml_slack_dag(): transform = DatacovesDbtOperator( task_id="transform", bash_command="dbt run -s personal_loans", - executor_config=TRANSFORM_CONFIG, ) @@ -155,11 +138,9 @@ dag = yaml_slack_dag() ```yaml description: "Sample DAG with Slack notification, custom image, and resource requests" -schedule_interval: "0 0 1 */12 *" +schedule: "0 0 1 */12 *" tags: - version_2 - - slack_notification - - blue_green default_args: start_date: 2023-01-01 owner: Noel Gomez @@ -186,11 +167,6 @@ nodes: transform: operator: operators.datacoves.dbt.DatacovesDbtOperator type: task - config: - image: datacoves/airflow-pandas:latest - resources: - memory: 8Gi - cpu: 1000m bash_command: "dbt run -s personal_loans" ``` diff --git a/docs/how-tos/airflow/sync-database.md b/docs/how-tos/airflow/sync-database.md index f247fd9..8b9af04 100644 --- a/docs/how-tos/airflow/sync-database.md +++ b/docs/how-tos/airflow/sync-database.md @@ -32,7 +32,7 @@ from operators.datacoves.data_sync import DatacovesDataSyncOperatorSnowflake @dag( default_args={"start_date": "2021-01"}, description="sync_airflow_db", - schedule_interval="0 0 1 */12 *", + schedule="0 0 1 */12 *", tags=["version_1"], catchup=False, ) diff --git a/docs/how-tos/airflow/use-variables-and-connections.md b/docs/how-tos/airflow/use-variables-and-connections.md index dc8c2ad..c6e639c 100644 --- a/docs/how-tos/airflow/use-variables-and-connections.md +++ b/docs/how-tos/airflow/use-variables-and-connections.md @@ -26,7 +26,7 @@ daily_run_tag = Variable.get("DBT_DAILY_RUN_TAG") @dag( default_args={"start_date": "2021-01"}, description="DAG that outputs a Variable", - schedule_interval="0 0 1 */12 *", + schedule="0 0 1 */12 *", tags=["version_1"], catchup=False, ) @@ -73,7 +73,7 @@ airbyte_connection = Connection.get_connection_from_secrets(conn_id="AIRBYTE_CON @dag( default_args={"start_date": "2021-01"}, description="DAG that outputs Airbyte Hostname", - schedule_interval="0 0 1 */12 *", + schedule="0 0 1 */12 *", tags=["version_1"], catchup=False, ) diff --git a/docs/how-tos/datahub/README.md b/docs/how-tos/datahub/README.md index 56239d3..262a338 100644 --- a/docs/how-tos/datahub/README.md +++ b/docs/how-tos/datahub/README.md @@ -2,4 +2,4 @@ These how to guides are dedicated to DataHub in Datacoves. See how to: -- Manage your DataHub instance from the VSCode terminal \ No newline at end of file +- Manage your DataHub instance from the VS Code terminal \ No newline at end of file diff --git a/docs/how-tos/datahub/how_to_datahub_cli.md b/docs/how-tos/datahub/how_to_datahub_cli.md index ba413b5..581c88f 100644 --- a/docs/how-tos/datahub/how_to_datahub_cli.md +++ b/docs/how-tos/datahub/how_to_datahub_cli.md @@ -1,32 +1,43 @@ -# How to use DataHub's CLI from your VSCode terminal +# How to use DataHub's CLI from your VS Code terminal -Connecting to your DataHub instance via your VSCode terminal might be really helpful to perform some maintenance in your metadata, run ingestions, delete data, etc. +Connecting to your DataHub instance via your VS Code terminal can be extremely useful for performing maintenance on your metadata, running ingestions, deleting data, and more. ## Configure DataHub CLI -### DataHub host url +### Setting the DataHub Host URL -First of all, you need to establish a secure connection to your DataHub server. +To establish a secure connection to your DataHub server, follow these steps: -Open a terminal on VSCode and run `datahub init`: +1. Open a terminal in VS Code and run the following command: +```bash +datahub init +``` ![DataHub init](assets/datahub-init.png) -Type the DataHub host url, following this pattern: `http://{environment slug}-datahub-datahub-gms:8080`, the environment slug can be found next to your environment name in the header. +2. When prompted, enter the DataHub host URL using the following pattern: -![Environment slug](assets/datahub-env-slug.png) +```bash + http://{environment-slug}-datahub-datahub-gms:8080 + ``` + + >[!TIP] The environment slug can be found next to your environment name on the top left corner of your Datacoves workspace. For example, the environment slug below is `DEV123`, so the URL would be: `http://dev123-datahub-datahub-gms:8080` -In this example it's `DEV123`, so the url becomes: http://dev123-datahub-datahub-gms:8080. +![Environment slug](assets/datahub-env-slug.png) -### DataHub API token +### Obtaining and Using a DataHub API Token -The next element you need to provide is a DataHub access token: +Next, you will be prompted to provide a DataHub access token to authenticate your connection. ![DataHub token](assets/datahub-token.png) -1. On a new tab, navigate to Datacoves and then DataHub +**Please follow these steps:** + +1. Open a new tab, navigate to Datacoves, head to the Observe tab within your environment, and click on DataHub. + 2. Go to `Settings` (gear icon on the top right corner) + 3. Click on the `Access Tokens` nav bar menu item ![DataHub access tokens](assets/datahub-access-tokens.png) @@ -35,12 +46,14 @@ The next element you need to provide is a DataHub access token: ![DataHub new token](assets/datahub-new-token.png) -5. Click on create and right after you'll see the new JWT token generated. -6. Copy the token clicking on this button and don't close the window as you won’t be able to see it again. +5. Click on create. Immediately after you will get a popup with the new token. Please don't close the window as you won’t be able to see it again. + +6. Copy the token clicking on the copy button. ![DataHub copy token](assets/datahub-copy-token.png) -7. Go back to the tab were you have VSCode terminal waiting for your input and paste the copied token. Press Enter. +7. Go back to the tab were you have VS Code terminal waiting for your input and paste the copied token. Press Enter. + 8. You can validate that the connection was correctly configured by running `datahub check server-config`: ![DataHub check](assets/datahub-check.png) @@ -55,3 +68,4 @@ Sometimes you loaded some data for testing purposes and the DataHub UI does not The command accepts different filters, a straight-forward one is `--platform`, i.e. `datahub delete --platform dbt`. +The command accepts different filters. A straightforward one is `--platform`, for example, `datahub delete --platform dbt`. \ No newline at end of file

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