Run Datalake using the metadata CLI
Stage | Metadata | Query Usage | Data Profiler | Data Quality | Lineage | DBT | Supported Versions |
---|---|---|---|---|---|---|---|
PROD | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | -- |
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 later To 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 Bucket Policy in AWS requires at least these permissions:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::<my bucket>",
"arn:aws:s3:::<my bucket>/*"
]
}
]
}
Python Requirements
If running OpenMetadata version greater than 0.13, you will need to install the Datalake ingestion for GCS or S3:
S3 installation
pip3 install "openmetadata-ingestion[datalake-s3]"
GCS installation
pip3 install "openmetadata-ingestion[datalake-gcs]"
Azure installation
pip3 install "openmetadata-ingestion[datalake-azure]"
If version <0.13
You will be installing the requirements together for S3 and GCS
pip3 install "openmetadata-ingestion[datalake]"
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
Source Configuration - Source Config using AWS S3
This is a sample config for Datalake using AWS S3:
source:
type: datalake
serviceName: local_datalake
serviceConnection:
config:
type: Datalake
configSource:
securityConfig:
awsAccessKeyId: aws access key id
awsSecretAccessKey: aws secret access key
awsRegion: aws region
bucketName: bucket name
prefix: prefix
sourceConfig:
type: DatabaseMetadata
config:
tableFilterPattern:
includes:
- ''
sink:
type: metadata-rest
config: {}
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
The sourceConfig
is defined here.
- 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
schemaFilterPattern
andtableFilterPattern
both support regex asinclude
orexclude
. E.g.,
Source Configuration - Service Connection using GCS
This is a sample config for Datalake using GCS:
source:
type: datalake
serviceName: local_datalake
serviceConnection:
config:
type: Datalake
configSource:
securityConfig:
gcsConfig:
type: type of account
projectId: project id
privateKeyId: private key id
privateKey: private key
clientEmail: client email
clientId: client id
authUri: https://accounts.google.com/o/oauth2/auth
tokenUri: https://oauth2.googleapis.com/token
authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs
clientX509CertUrl: clientX509 Certificate Url
bucketName: bucket name
prefix: prefix
sourceConfig:
config:
tableFilterPattern:
includes:
- ''
sink:
type: metadata-rest
config: {}
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
The sourceConfig
is defined here.
- 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 - Service Connection using Azure
This is a sample config for Datalake using Azure:
# Datalake with Azure
source:
type: datalake
serviceName: local_datalake
serviceConnection:
config:
type: Datalake
configSource:
securityConfig:
clientId: client-id
clientSecret: client-secret
tenantId: tenant-id
accountName: account-name
prefix: prefix
sourceConfig:
config:
tableFilterPattern:
includes:
- ''
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
The sourceConfig
is defined here.
- 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
schemaFilterPattern and tableFilternPattern: Note that the schemaFilterPattern
and tableFilterPattern
both support regex as include
or exclude
. 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 they support regex as include or exclude. E.g.,
tableFilterPattern:
includes:
- users
- type_test
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:
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: openmetadata
securityConfig:
jwtToken: '{bot_jwt_token}'
We support different security providers. You can find their definitions here. You can find the different implementation of the ingestion below.
2. Run with the CLI
First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
metadata ingest -c <path-to-yaml>
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.
dbt Integration
You can learn more about how to ingest dbt models' definitions and their lineage here.