**catalog**: Catalog of the data source(Example: hive_metastore). This is optional parameter, if you would like to restrict the metadata reading to a single catalog. When left blank, OpenMetadata Ingestion attempts to scan all the catalog.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**databaseSchema**: DatabaseSchema of the data source. This is optional parameter, if you would like to restrict the metadata reading to a single databaseSchema. When left blank, OpenMetadata Ingestion attempts to scan all the databaseSchema.
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**hostPort**: Enter the fully qualified hostname and port number for your Databricks deployment in the Host and Port field.
{% /codeInfo %}
{% codeInfo srNumber=4 %}
**token**: Generated Token to connect to Databricks.
{% /codeInfo %}
{% codeInfo srNumber=5 %}
**httpPath**: Databricks compute resources URL.
{% /codeInfo %}
{% codeInfo srNumber=6 %}
**connectionTimeout**: The maximum amount of time (in seconds) to wait for a successful connection to the data source. If the connection attempt takes longer than this timeout period, an error will be returned.
**useUnityCatalog**: Enable this flag to extract the metadata and lineage information using databricks unity catalog instead of using legacy hive metastore. When you enable this flag make sure you have enabled the unity catalog on your instance.
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)
**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"`
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.
## Query Usage
The Query Usage workflow will be using the `query-parser` processor.
After running a Metadata Ingestion workflow, we can run Query Usage 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.
**Note**: To get Query Usage and Lineage details, need a Azure Databricks Premium account.
### 1. Define the YAML Config
This is a sample config for Databricks Usage:
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=12 %}
#### 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/databaseServiceQueryUsagePipeline.json).
**queryLogDuration**: Configuration to tune how far we want to look back in query logs to process usage data.
{% /codeInfo %}
{% codeInfo srNumber=13 %}
**stageFileLocation**: Temporary file name to store the query logs before processing. Absolute file path required.
{% /codeInfo %}
{% codeInfo srNumber=14 %}
**resultLimit**: Configuration to set the limit for query logs
{% /codeInfo %}
{% codeInfo srNumber=15 %}
**queryLogFilePath**: Configuration to set the file path for query logs
{% /codeInfo %}
{% codeInfo srNumber=16 %}
#### Processor, Stage and Bulk Sink Configuration
To specify where the staging files will be located.
Note that the location is a directory that will be cleaned at the end of the ingestion.
{% /codeInfo %}
{% codeInfo srNumber=17 %}
#### 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: databricks-usage
serviceName: <servicename>
sourceConfig:
config:
type: DatabaseUsage
```
```yaml {% srNumber=12 %}
# Number of days to look back
queryLogDuration: 7
```
```yaml {% srNumber=13 %}
# This is a directory that will be DELETED after the usage runs
stageFileLocation: <pathtostorethestagefile>
```
```yaml {% srNumber=14 %}
# resultLimit: 1000
```
```yaml {% srNumber=15 %}
# If instead of getting the query logs from the database we want to pass a file with the queries
# queryLogFilePath: path-to-file
```
```yaml {% srNumber=16 %}
processor:
type: query-parser
config: {}
stage:
type: table-usage
config:
filename: /tmp/databricks_usage
bulkSink:
type: metadata-usage
config:
filename: /tmp/databricks_usage
```
```yaml {% srNumber=17 %}
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadatahostandport>
authProvider: <OpenMetadataauthprovider>
```
{% /codeBlock %}
{% /codePreview %}
### 2. Run with the CLI
There is an extra requirement to run the Usage pipelines. You will need to install:
After saving the YAML config, we will run the command the same way we did for the metadata ingestion:
```bash
metadata ingest -c <path-to-yaml>
```
## 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=18 %}
#### 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=19 %}
**profileSample**: Percentage of data or no. of rows we want to execute the profiler and tests on.
{% /codeInfo %}
{% codeInfo srNumber=20 %}
**threadCount**: Number of threads to use during metric computations.
{% /codeInfo %}
{% codeInfo srNumber=21 %}
**processPiiSensitive**: Optional configuration to automatically tag columns that might contain sensitive information.
{% /codeInfo %}
{% codeInfo srNumber=22 %}
**confidence**: Set the Confidence value for which you want the column to be marked
{% /codeInfo %}
{% codeInfo srNumber=23 %}
**timeoutSeconds**: Profiler Timeout in Seconds
{% /codeInfo %}
{% codeInfo srNumber=24 %}
**databaseFilterPattern**: Regex to only fetch databases that matches the pattern.
{% /codeInfo %}
{% codeInfo srNumber=25 %}
**schemaFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
{% /codeInfo %}
{% codeInfo srNumber=26 %}
**tableFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
{% /codeInfo %}
{% codeInfo srNumber=27 %}
#### 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=28 %}
#### Sink Configuration
To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
{% /codeInfo %}
{% codeInfo srNumber=29 %}
#### 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: databricks
serviceName: local_databricks
sourceConfig:
config:
type: Profiler
```
```yaml {% srNumber=18 %}
generateSampleData: true
```
```yaml {% srNumber=19 %}
# profileSample: 85
```
```yaml {% srNumber=20 %}
# threadCount: 5
```
```yaml {% srNumber=21 %}
processPiiSensitive: false
```
```yaml {% srNumber=22 %}
# confidence: 80
```
```yaml {% srNumber=23 %}
# timeoutSeconds: 43200
```
```yaml {% srNumber=24 %}
# databaseFilterPattern:
# includes:
# - database1
# - database2
# excludes:
# - database3
# - database4
```
```yaml {% srNumber=25 %}
# schemaFilterPattern:
# includes:
# - schema1
# - schema2
# excludes:
# - schema3
# - schema4
```
```yaml {% srNumber=26 %}
# tableFilterPattern:
# includes:
# - table1
# - table2
# excludes:
# - table3
# - table4
```
```yaml {% srNumber=27 %}
processor:
type: orm-profiler
config: {} # Remove braces if adding properties
# tableConfig:
# - fullyQualifiedName: <tablefqn>
# profileSample: <numberbetween0and99> # default
# profileSample: <numberbetween0and99> # default will be 100 if omitted
- 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. Prepare the Profiler DAG
Here, we follow a similar approach as with the metadata and usage pipelines, although we will use a different Workflow class:
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=30 %}
#### Import necessary modules
The `ProfilerWorkflow` class that is being imported is a part of a metadata orm_profiler framework, which defines a process of extracting Profiler data.
Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.
{% /codeInfo %}
{% codeInfo srNumber=31 %}
**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.
{% /codeInfo %}
{% codeInfo srNumber=32 %}
- **config**: Specifies config for the profiler as we prepare above.
{% /codeInfo %}
{% codeInfo srNumber=33 %}
- **metadata_ingestion_workflow()**: This code defines a function `metadata_ingestion_workflow()` that loads a YAML configuration, creates a `ProfilerWorkflow` object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.
{% /codeInfo %}
{% codeInfo srNumber=34 %}
- **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](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html#declaring-a-dag).
