Run Presto using the Airflow SDK

FeatureStatus
StagePROD
Metadata
Query Usage
Data Profiler
Data Quality
LineagePartially via Views
DBT
Supported Versions--
FeatureStatus
LineagePartially via Views
Table-level
Column-level

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

Configure and schedule Presto metadata and profiler workflows from the OpenMetadata UI:

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.

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

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

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

This is a sample config for Presto:

Username: Specify the User to connect to Presto. It should have enough privileges to read all the metadata.

password: Password to connect to Presto.

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

catalog: Presto offers a catalog feature where all the databases are stored. (Providing the Catalog is not mandatory from 0.12.2 or greater versions)

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.

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

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

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:

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

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

  • 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.

Create a Python file in your Airflow DAGs directory with the following contents:

The Workflow class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.

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 metadata ingestion as we prepare above.
  • metadata_ingestion_workflow(): This code defines a function metadata_ingestion_workflow() that loads a YAML configuration, creates a Workflow 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.

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.

filename.py

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.

This is a sample config for the profiler:

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.

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.

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

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

Here, we follow a similar approach as with the metadata and usage pipelines, although we will use a different Workflow class:

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