OpenMetadata/openmetadata-docs/content/v1.1.0/connectors/database/sap-hana/cli.md

---
title: Run SAP Hana Connector using the CLI
slug: /connectors/database/sap-hana/cli
---

# Run SAP Hana using the metadata CLI

{% multiTablesWrapper %}

| Feature            | Status                       |
| :----------------- |:-----------------------------|
| Stage              | BETA                         |
| Metadata           | {% icon iconName="check" /%} |
| Query Usage        | {% icon iconName="cross" /%} |
| Data Profiler      | {% icon iconName="check" /%} |
| Data Quality       | {% icon iconName="check" /%} |
| Lineage            | Partially via Views          |
| DBT                | {% icon iconName="cross" /%} |

| 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 SAP Hana connector.

Configure and schedule SAP Hana metadata and profiler workflows from the OpenMetadata UI:

- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
- [Data Profiler](#data-profiler)
- [dbt Integration](#dbt-integration)

## Requirements

{%inlineCallout icon="description" bold="OpenMetadata 1.1 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.

{% note %}
The connector is compatible with HANA or HANA express versions since HANA SPS 2.
{% /note %}

### Python Requirements

To run the SAP Hana ingestion, you will need to install:

```bash
pip3 install "openmetadata-ingestion[sap-hana]"
```

## 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/sapHanaConnection.json)
you can find the structure to create a connection to SAP Hana.

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 SAP Hana:

{% codePreview %}

{% codeInfoContainer %}

#### Source Configuration - Service Connection

We support two possible connection types:
1. **SQL Connection**, where you will the username, password and host.
2. **HDB User Store** [connection](https://help.sap.com/docs/SAP_HANA_PLATFORM/b3ee5778bc2e4a089d3299b82ec762a7/dd95ac9dbb571014a7d7f0234d762fdb.html?version=2.0.05&locale=en-US).
   Note that the HDB Store will need to be locally available to the instance running the ingestion process.
   If you are unsure about this setting, you can run the ingestion process passing the usual SQL connection details.

##### SQL Connection

If using the SQL Connection, inform:

{% codeInfo srNumber=1 %}

**hostPort**: Host and port of the SAP Hana service. This should be specified as a string in the format `hostname:port`. E.g., `localhost:39041`, `host.docker.internal:39041`.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

**username**: Specify the User to connect to SAP Hana. It should have enough privileges to read all the metadata.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

**password**: Password to connect to SAP Hana.

{% /codeInfo %}

{% codeInfo srNumber=4 %}

**database**: Optional parameter to connect to a specific database.

{% /codeInfo %}

{% codeInfo srNumber=5 %}

**databaseSchema**: databaseSchema of the data source. This is an optional parameter, if you would like to restrict the metadata reading to a single schema. When left blank, OpenMetadata Ingestion attempts to scan all the schemas.

{% /codeInfo %}

##### HDB User Store

If you have a User Store configured, then:

{% codeInfo srNumber=6 %}

**userKey**: HDB Store User Key generated from the command `hdbuserstore SET <KEY> <host:port> <USERNAME> <PASSWORD>`.

{% /codeInfo %}

#### Source Configuration - Source Config

{% codeInfo srNumber=9 %}

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 filter supports regex as include or exclude. You can find examples [here](/connectors/ingestion/workflows/metadata/filter-patterns/database)

{% /codeInfo %}


#### Sink Configuration

{% codeInfo srNumber=10 %}

To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.

{% /codeInfo %}

{% partial file="workflow-config.md" /%}

#### Advanced Configuration

{% codeInfo srNumber=7 %}

**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=8 %}

**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"`

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

```yaml
source:
  type: sapHana
  serviceName: <service name>
  serviceConnection:
    config:
      type: SapHana
      connection:
```
```yaml {% srNumber=1 %}
        ## Parameters for the SQL Connection
        # hostPort: <hostPort>
```
```yaml {% srNumber=2 %}
        # username: <username>
```
```yaml {% srNumber=3 %}
        # password: <password>
```
```yaml {% srNumber=4 %}
        # database: <database>
```
```yaml {% srNumber=5 %}
        # databaseSchema: <schema>
```
```yaml {% srNumber=6 %}
        ## Parameter for the HDB User Store
        # userKey: <key>
```
```yaml {% srNumber=7 %}
      # connectionOptions:
      #   key: value
```
```yaml {% srNumber=8 %}
      # connectionArguments:
      #   key: value
```
```yaml {% srNumber=9 %}
  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:
      #     - table1
      #     - table2
      #   excludes:
      #     - table3
      #     - table4
```

```yaml {% srNumber=10 %}
sink:
  type: metadata-rest
  config: {}
```

{% partial file="workflow-config-yaml.md" /%}

{% /codeBlock %}

{% /codePreview %}

### 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.

## Data Profiler

The Data Profiler workflow will be using the `orm-profiler` processor.

After running a Metadata Ingestion workflow, we can run Data Profiler workflow.
While the `serviceName` will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the `serviceConnection` details from the server.


### 1. Define the YAML Config

This is a sample config for the profiler:

{% codePreview %}

{% codeInfoContainer %}

{% codeInfo srNumber=15 %}
#### Source Configuration - Source Config

You can find all the definitions and types for the  `sourceConfig` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceProfilerPipeline.json).

**generateSampleData**: Option to turn on/off generating sample data.

{% /codeInfo %}

{% codeInfo srNumber=16 %}

**profileSample**: Percentage of data or no. of rows we want to execute the profiler and tests on.

{% /codeInfo %}

{% codeInfo srNumber=17 %}

**threadCount**: Number of threads to use during metric computations.

{% /codeInfo %}

{% codeInfo srNumber=18 %}

**processPiiSensitive**: Optional configuration to automatically tag columns that might contain sensitive information.

{% /codeInfo %}

{% codeInfo srNumber=19 %}

**confidence**: Set the Confidence value for which you want the column to be marked

{% /codeInfo %}


{% codeInfo srNumber=20 %}

**timeoutSeconds**: Profiler Timeout in Seconds

{% /codeInfo %}

{% codeInfo srNumber=21 %}

**databaseFilterPattern**: Regex to only fetch databases that matches the pattern.

{% /codeInfo %}

{% codeInfo srNumber=22 %}

**schemaFilterPattern**: Regex to only fetch tables or databases that matches the pattern.

{% /codeInfo %}

{% codeInfo srNumber=23 %}

**tableFilterPattern**: Regex to only fetch tables or databases that matches the pattern.

{% /codeInfo %}

{% codeInfo srNumber=24 %}

#### Processor Configuration

Choose the `orm-profiler`. Its config can also be updated to define tests from the YAML itself instead of the UI:

**tableConfig**: `tableConfig` allows you to set up some configuration at the table level.
{% /codeInfo %}


{% codeInfo srNumber=25 %}

#### Sink Configuration

To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
{% /codeInfo %}


{% codeInfo srNumber=26 %}

#### 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:

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}


```yaml
source:
  type: sapHana
  serviceName: <service name>
  sourceConfig:
    config:
      type: Profiler
```

```yaml {% srNumber=15 %}
      generateSampleData: true
```
```yaml {% srNumber=16 %}
      # profileSample: 85
```
```yaml {% srNumber=17 %}
      # threadCount: 5
```
```yaml {% srNumber=18 %}
      processPiiSensitive: false
```
```yaml {% srNumber=19 %}
      # confidence: 80
```
```yaml {% srNumber=20 %}
      # timeoutSeconds: 43200
```
```yaml {% srNumber=21 %}
      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4
```
```yaml {% srNumber=22 %}
      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4
```
```yaml {% srNumber=23 %}
      # tableFilterPattern:
      #   includes:
      #     - table1
      #     - table2
      #   excludes:
      #     - table3
      #     - table4
```

```yaml {% srNumber=24 %}
processor:
  type: orm-profiler
  config: {}  # Remove braces if adding properties
    # tableConfig:
    #   - fullyQualifiedName: <table fqn>
    #     profileSample: <number between 0 and 99> # default 

    #     profileSample: <number between 0 and 99> # default will be 100 if omitted
    #     profileQuery: <query to use for sampling data for the profiler>
    #     columnConfig:
    #       excludeColumns:
    #         - <column name>
    #       includeColumns:
    #         - columnName: <column name>
    #         - metrics:
    #           - MEAN
    #           - MEDIAN
    #           - ...
    #     partitionConfig:
    #       enablePartitioning: <set to true to use partitioning>
    #       partitionColumnName: <partition column name. Must be a timestamp or datetime/date field type>
    #       partitionInterval: <partition interval>
    #       partitionIntervalUnit: <YEAR, MONTH, DAY, HOUR>

```

```yaml {% srNumber=25 %}
sink:
  type: metadata-rest
  config: {}
```

```yaml {% srNumber=26 %}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>
```

{% /codeBlock %}

{% /codePreview %}

- You can learn more about how to configure and run the Profiler Workflow to extract Profiler data and execute the Data Quality from [here](/connectors/ingestion/workflows/profiler)

### 2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

```bash
metadata profile -c <path-to-yaml>
```

Note how instead of running `ingest`, we are using the `profile` command to select the Profiler workflow.

## 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/sap-hana/airflow"
  / %}

{% /tilesContainer %}