Run MSSQL using the metadata CLI

Feature	Status
Stage	PROD
Metadata
Query Usage
Data Profiler
Data Quality
Lineage
DBT
Supported Versions	--

Feature	Status
Lineage
Table-level
Column-level

In this section, we provide guides and references to use the MSSQL connector.

Configure and schedule MSSQL 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.

MSSQL User must grant SELECT privilege to fetch the metadata of tables and views.

Python Requirements

To run the MSSQL ingestion, you will need to install:

If you want to run the Usage Connector, you'll also need to install:

Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to MSSQL.

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

This is a sample config for MSSQL:

Source Configuration - Service Connection

scheme: Defines how to connect to MSSQL. We support mssql+pytds, mssql+pyodbc, and mssql+pymssql.

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

password: Password to connect to MSSQL.

hostPort: Enter the fully qualified hostname and port number for your MSSQL deployment in the Host and Port field.

Database: The database of the data source is an optional parameter, if you would like to restrict the metadata reading to a single database. If left blank, OpenMetadata ingestion attempts to scan all the databases.

uriString: In case of a pyodbc connection.

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 filter supports regex as include or exclude. You can find examples here

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:

Advanced Configuration

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.

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"

filename.yaml

Workflow Configs for Security Provider

We support different security providers. You can find their definitions here.

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.

You can refer to the JWT Troubleshooting section link 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.

2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

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.

1. Define the YAML Config

This is a sample config for MSSQL Usage:

Source Configuration - Source Config

You can find all the definitions and types for the sourceConfig here.

queryLogDuration: Configuration to tune how far we want to look back in query logs to process usage data.

stageFileLocation: Temporary file name to store the query logs before processing. Absolute file path required.

resultLimit: Configuration to set the limit for query logs

queryLogFilePath: Configuration to set the file path for query logs

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.

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:

filename.yaml

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:

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:

Source Configuration - Source Config

You can find all the definitions and types for the sourceConfig here.

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

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

threadCount: Number of threads to use during metric computations.

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

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

timeoutSeconds: Profiler Timeout in Seconds

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

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

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

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.

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:

filename.yaml

You can learn more about how to configure and run the Profiler Workflow to extract Profiler data and execute the Data Quality from here

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:

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.

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.

config: Specifies config for the profiler as we prepare above.

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.

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.

filename.py

Lineage

You can learn more about how to ingest lineage here.

dbt Integration

You can learn more about how to ingest dbt models' definitions and their lineage here.