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Run the Delta Lake Connector Externally | /connectors/database/deltalake/yaml |
{% connectorDetailsHeader name="DeltaLake" stage="PROD" platform="OpenMetadata" availableFeatures=["Metadata", "dbt"] unavailableFeatures=["Query Usage", "Data Profiler", "Data Quality", "Lineage", "Column-level Lineage", "Owners", "Tags", "Stored Procedures"] / %}
In this section, we provide guides and references to use the Deltalake connector.
Configure and schedule Deltalake metadata and profiler workflows from the OpenMetadata UI:
{% partial file="/v1.5/connectors/external-ingestion-deployment.md" /%}
Requirements
Deltalake requires to run with Python 3.8, 3.9 or 3.10. We do not yet support the Delta connector for Python 3.11
Python Requirements
{% partial file="/v1.5/connectors/python-requirements.md" /%}
To run the Deltalake ingestion, you will need to install:
- If extracting from a metastore
pip3 install "openmetadata-ingestion[deltalake-spark]"
- If extracting directly from the storage
pip3 install "openmetadata-ingestion[deltalake-storage]"
Metadata Ingestion
All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Deltalake.
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 - From Metastore
{% codePreview %}
{% codeInfoContainer %}
Source Configuration - Service Connection
{% codeInfo srNumber=1 %}
Metastore Host Port: Enter the Host & Port of Hive Metastore Service to configure the Spark Session. Either
of metastoreHostPort
, metastoreDb
or metastoreFilePath
is required.
Metastore File Path: Enter the file path to local Metastore in case Spark cluster is running locally. Either
of metastoreHostPort
, metastoreDb
or metastoreFilePath
is required.
Metastore DB: The JDBC connection to the underlying Hive metastore DB. Either
of metastoreHostPort
, metastoreDb
or metastoreFilePath
is required.
appName (Optional): Enter the app name of spark session.
Connection Arguments (Optional): Key-Value pairs that will be used to pass extra config
elements to the Spark
Session builder.
We are internally running with pyspark
3.X and delta-lake
2.0.0. This means that we need to consider Spark
configuration options for 3.X.
Metastore Host Port
When connecting to an External Metastore passing the parameter Metastore Host Port
, we will be preparing a Spark Session with the configuration
.config("hive.metastore.uris", "thrift://{connection.metastoreHostPort}")
Then, we will be using the catalog
functions from the Spark Session to pick up the metadata exposed by the Hive Metastore.
Metastore File Path
If instead we use a local file path that contains the metastore information (e.g., for local testing with the default metastore_db
directory), we will set
.config("spark.driver.extraJavaOptions", "-Dderby.system.home={connection.metastoreFilePath}")
To update the Derby
information. More information about this in a great SO thread.
- You can find all supported configurations here
- If you need further information regarding the Hive metastore, you can find it here, and in The Internals of Spark SQL book.
Metastore Database
You can also connect to the metastore by directly pointing to the Hive Metastore db, e.g., jdbc:mysql://localhost:3306/demo_hive
.
Here, we will need to inform all the common database settings (url, username, password), and the driver class name for JDBC metastore.
You will need to provide the driver to the ingestion image, and pass the classpath
which will be used in the Spark Configuration under spark.driver.extraClassPath
.
{% /codeInfo %}
{% partial file="/v1.5/connectors/yaml/database/source-config-def.md" /%}
{% partial file="/v1.5/connectors/yaml/ingestion-sink-def.md" /%}
{% partial file="/v1.5/connectors/yaml/workflow-config-def.md" /%}
Advanced Configuration
{% codeInfo srNumber=2 %}
Connection Options (Optional): Enter the details for any additional connection options that can be sent to database during the connection. These details must be added as Key-Value pairs.
{% /codeInfo %}
{% codeInfo srNumber=3 %}
Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to database 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
authenticator
details in the Connection Arguments as a Key-Value pair as follows:"authenticator" : "sso_login_url"
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="filename.yaml" %}
source:
type: deltalake
serviceName: "<service name>"
serviceConnection:
config:
type: DeltaLake
configSource:
connection:
# Pick only of these
## 1. Hive Service Thrift Connection
metastoreHostPort: "<metastore host port>"
## 2. Hive Metastore db connection
# metastoreDb: jdbc:mysql://localhost:3306/demo_hive
# username: username
# password: password
# driverName: org.mariadb.jdbc.Driver
# jdbcDriverClassPath: /some/path/
## 3. Local file for Testing
# metastoreFilePath: "<path_to_metastore>/metastore_db"
appName: MyApp
# connectionOptions:
# key: value
# connectionArguments:
# key: value
{% partial file="/v1.5/connectors/yaml/database/source-config.md" /%}
{% partial file="/v1.5/connectors/yaml/ingestion-sink.md" /%}
{% partial file="/v1.5/connectors/yaml/workflow-config.md" /%}
{% /codeBlock %}
{% /codePreview %}
Source Configuration - From Storage - S3
{% codePreview %}
{% codeInfoContainer %}
Source Configuration - Service Connection
{% codeInfo srNumber=1 %}
- 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 tableFilterPattern: Note that the
schemaFilterPattern
andtableFilterPattern
both support regex asinclude
orexclude
. E.g.,
{% /codeInfo %}
{% partial file="/v1.5/connectors/yaml/database/source-config-def.md" /%}
{% partial file="/v1.5/connectors/yaml/ingestion-sink-def.md" /%}
{% partial file="/v1.5/connectors/yaml/workflow-config-def.md" /%}
{% /codeInfoContainer %}
{% codeBlock fileName="filename.yaml" %}
source:
type: deltalake
serviceName: <service_name>
serviceConnection:
config:
type: DeltaLake
configSource:
connection:
securityConfig:
awsAccessKeyId: aws access key id
awsSecretAccessKey: aws secret access key
awsRegion: aws region
bucketName: bucket name
prefix: prefix
{% partial file="/v1.5/connectors/yaml/database/source-config.md" /%}
{% partial file="/v1.5/connectors/yaml/ingestion-sink.md" /%}
{% partial file="/v1.5/connectors/yaml/workflow-config.md" /%}
{% /codeBlock %}
{% /codePreview %}
{% partial file="/v1.5/connectors/yaml/ingestion-cli.md" /%}
dbt Integration
{% tilesContainer %}
{% tile icon="mediation" title="dbt Integration" description="Learn more about how to ingest dbt models' definitions and their lineage." link="/connectors/ingestion/workflows/dbt" /%}
{% /tilesContainer %}