Run Datalake 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 Datalake connector.
Configure and schedule Datalake 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.
Note: Datalake connector supports extracting metadata from file types JSON, CSV, TSV & Parquet.
S3 Permissions
To execute metadata extraction AWS account should have enough access to fetch required data. The <strong>Bucket Policy</strong> in AWS requires at least these permissions:
ADLS Permissions
To extract metadata from Azure ADLS (Storage Account - StorageV2), you will need an App Registration with the following permissions on the Storage Account:
- Storage Blob Data Contributor
- Storage Queue Data Contributor
Python Requirements
If running OpenMetadata version greater than 0.13, you will need to install the Datalake ingestion for GCS or S3:
S3 installation
GCS installation
Azure installation
If version <0.13
You will be installing the requirements together for S3 and GCS
Metadata Ingestion
All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Datalake.
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 Datalake using AWS S3:
Source Configuration - Service Connection
- awsAccessKeyId: Enter your secure access key ID for your DynamoDB connection. The specified key ID should be authorized to read all databases you want to include in the metadata ingestion workflow.
- awsSecretAccessKey: Enter the Secret Access Key (the passcode key pair to the key ID from above).
- awsRegion: Specify the region in which your DynamoDB is located. This setting is required even if you have configured a local AWS profile.
- schemaFilterPattern and tableFilternPattern: Note that the
schemaFilterPatternandtableFilterPatternboth support regex asincludeorexclude. E.g.,
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:
This is a sample config for Datalake using GCS:
Source Configuration - Service Connection
- type: Credentials type, e.g.
service_account. - projectId
- privateKey
- privateKeyId
- clientEmail
- clientId
- authUri: https://accounts.google.com/o/oauth2/auth by default
- tokenUri: https://oauth2.googleapis.com/token by default
- authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs by default
- clientX509CertUrl
- bucketName: name of the bucket in GCS
- Prefix: prefix in gcs bucket
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:
This is a sample config for Datalake using Azure:
Source Configuration - Service Connection
- Client ID : Client ID of the data storage account
- Client Secret : Client Secret of the account
- Tenant ID : Tenant ID under which the data storage account falls
- Account Name : Account Name of the data Storage
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:
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.