Run Salesforce using the Airflow SDK
| Feature | Status |
|---|---|
| Stage | PROD |
| Metadata | |
| Query Usage | |
| Data Profiler | |
| Data Quality | |
| Lineage | |
| DBT | |
| Supported Versions | -- |
| Feature | Status |
|---|---|
| Lineage | |
| Table-level | |
| Column-level |
In this section, we provide guides and references to use the Salesforce connector.
Configure and schedule Salesforce metadata and profiler workflows from the OpenMetadata UI:
Requirements
OpenMetadata 0.12 or laterTo deploy OpenMetadata, check the Deployment guides.
To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment.
Following are the permissions you will require to fetch the metadata from Salesforce.
API Access: You must have the API Enabled permission in your Salesforce organization.
Object Permissions: You must have read access to the Salesforce objects that you want to ingest.
Python Requirements
To run the Salesforce ingestion, you will need to install:
Metadata Ingestion
All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Salesforce.
In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.
The workflow is modeled around the following JSON Schema
1. Define the YAML Config
This is a sample config for Salesforce:
Source Configuration - Service Connection
username: Username to connect to the Salesforce. This user should have the access as defined in requirements.
password: Password to connect to Salesforce.
securityToken: Salesforce Security Token is required to access the metadata through APIs. You can checkout this doc on how to get the security token.
sobjectName: Specify the Salesforce Object Name in case you want to ingest a specific object. If left blank, we will ingest all the Objects.
salesforceApiVersion: Follow the steps mentioned here to get the API version. Enter the numerical value in the field, For example 42.0.
salesforceDomain: When connecting to Salesforce, you can specify the domain to use for accessing the platform. The common domains include login and test, and you can also utilize Salesforce My Domain. By default, the domain login is used for accessing Salesforce.
Source Configuration - Source Config
The sourceConfig is defined here:
markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.
includeTables: true or false, to ingest table data. Default is true.
includeViews: true or false, to ingest views definitions.
databaseFilterPattern, schemaFilterPattern, tableFilternPattern: Note that the filter supports regex as include or exclude. You can find examples here
Sink Configuration
To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.
Workflow Configuration
The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.
For a simple, local installation using our docker containers, this looks like:
Advanced Configuration
Connection Options (Optional): Enter the details for any additional connection options that can be sent to Athena during the connection. These details must be added as Key-Value pairs.
Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Athena during the connection. These details must be added as Key-Value pairs.
- In case you are using Single-Sign-On (SSO) for authentication, add the
authenticatordetails in the Connection Arguments as a Key-Value pair as follows:"authenticator" : "sso_login_url" - In case you authenticate with SSO using an external browser popup, then add the
authenticatordetails in the Connection Arguments as a Key-Value pair as follows:"authenticator" : "externalbrowser"
Workflow Configs for Security Provider
We support different security providers. You can find their definitions here.
Openmetadata JWT Auth
- 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. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.
2. Prepare the Ingestion DAG
Create a Python file in your Airflow DAGs directory with the following contents:
Import necessary modules
The Workflow class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.
Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.
Default arguments for all tasks in the Airflow DAG.
- Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.
- config: Specifies config for the metadata ingestion as we prepare above.
- metadata_ingestion_workflow(): This code defines a function
metadata_ingestion_workflow()that loads a YAML configuration, creates aWorkflowobject, executes the workflow, checks its status, prints the status to the console, and stops the workflow.
- DAG: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
- For more Airflow DAGs creation details visit here.
Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.