Run Deltalake using the Airflow SDK
Feature | Status |
---|---|
Stage | PROD |
Metadata | |
Query Usage | |
Data Profiler | |
Data Quality | |
Lineage | Partially via Views |
DBT | |
Supported Versions | -- |
Feature | Status |
---|---|
Lineage | Partially via Views |
Table-level | |
Column-level |
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
OpenMetadata 0.12 or laterTo 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.
Python Requirements
To run the Deltalake ingestion, you will need to install:
Metadata Ingestion
All connectors are defined as JSON Schemas. Here 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
1. Define the YAML Config
This is a sample config for Deltalake:
Source Configuration - Service Connection
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
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
To update the Derby
information. More information about this in a great SO thread.
- You can find all supported configurations here
- If you need further information regarding the Hive metastore, you can find it here, and in The Internals of Spark SQL book.
Metastore Database
You can also connect to the metastore by directly pointing to the Hive Metastore db, e.g., jdbc:mysql://localhost:3306/demo_hive
.
Here, we will need to inform all the common database settings (url, username, password), and the driver class name for JDBC metastore.
You will need to provide the driver to the ingestion image, and pass the classpath
which will be used in the Spark Configuration under sparks.driver.extraClassPath
.
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"
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. Prepare the Ingestion DAG
Create a Python file in your Airflow DAGs directory with the following contents:
Import necessary modules
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 aWorkflow
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.