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---
title: Run DeltaLake Connector using the CLI
slug: /connectors/database/deltalake/cli
---
# Run Deltalake using the metadata CLI
{% multiTablesWrapper %}
| Feature | Status |
| :----------------- | :--------------------------- |
| Stage | PROD |
| Metadata | {% icon iconName="check" /%} |
| Query Usage | {% icon iconName="cross" /%} |
| Data Profiler | {% icon iconName="cross" /%} |
| Data Quality | {% icon iconName="cross" /%} |
| Lineage | Partially via Views |
| DBT | {% icon iconName="cross" /%} |
| Supported Versions | -- |
| Feature | Status |
| :----------- | :--------------------------- |
| Lineage | Partially via Views |
| Table-level | {% icon iconName="check" /%} |
| Column-level | {% icon iconName="check" /%} |
{% /multiTablesWrapper %}
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:
- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
- [dbt Integration](#dbt-integration)
## Requirements
{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%}
To deploy OpenMetadata, check the Deployment guides.
{%/inlineCallout%}
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.
### Python Requirements
To run the Deltalake ingestion, you will need to install:
```bash
pip3 install "openmetadata-ingestion[deltalake]"
```
## Metadata Ingestion
All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/deltaLakeConnection.json)
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](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)
### 1. Define the YAML Config
This is a sample config for Deltalake:
{% 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](https://stackoverflow.com/questions/38377188/how-to-get-rid-of-derby-log-metastore-db-from-spark-shell).
- You can find all supported configurations [here](https://spark.apache.org/docs/latest/configuration.html)
- If you need further information regarding the Hive metastore, you can find
it [here](https://spark.apache.org/docs/3.0.0-preview/sql-data-sources-hive-tables.html), and in The Internals of
Spark SQL [book](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-hive-metastore.html).
{% /codeInfo %}
#### Source Configuration - Source Config
{% codeInfo srNumber=4 %}
The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json):
**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.,
{% /codeInfo %}
#### Sink Configuration
{% codeInfo srNumber=5 %}
To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
{% /codeInfo %}
#### Workflow Configuration
{% codeInfo srNumber=6 %}
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:
{% /codeInfo %}
#### Advanced Configuration
{% codeInfo srNumber=2 %}
**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.
{% /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 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 `authenticator` details 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 `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "externalbrowser"`
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="filename.yaml" %}
```yaml
source:
type: deltalake
serviceName: "<service name>"
serviceConnection:
config:
type: DeltaLake
```
```yaml {% srNumber=1 %}
metastoreConnection:
# Pick only of the three
metastoreHostPort: "<metastore host port>"
# metastoreDb: jdbc:mysql://localhost:3306/demo_hive
# metastoreFilePath: "<path_to_metastore>/metastore_db"
appName: MyApp
```
```yaml {% srNumber=2 %}
# connectionOptions:
# key: value
```
```yaml {% srNumber=3 %}
# connectionArguments:
# key: value
```
```yaml {% srNumber=4 %}
sourceConfig:
config:
type: DatabaseMetadata
markDeletedTables: true
includeTables: true
includeViews: true
# includeTags: true
# databaseFilterPattern:
# includes:
# - database1
# - database2
# excludes:
# - database3
# - database4
# schemaFilterPattern:
# includes:
# - schema1
# - schema2
# excludes:
# - schema3
# - schema4
# tableFilterPattern:
# includes:
# - users
# - type_test
# excludes:
# - table3
# - table4
```
```yaml {% srNumber=5 %}
sink:
type: metadata-rest
config: {}
```
```yaml {% srNumber=6 %}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
```
{% /codeBlock %}
{% /codePreview %}
### Workflow Configs for Security Provider
We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client).
## 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](/deployment/security/enable-jwt-tokens).
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
```
- You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) 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](/deployment/security/workflow-config-auth).
### 2. Run with the CLI
First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
```bash
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
{% 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 %}
## Related
{% tilesContainer %}
{% tile
title="Ingest with Airflow"
description="Configure the ingestion using Airflow SDK"
link="/connectors/database/deltalake/airflow"
/ %}
{% /tilesContainer %}