acryl-datahub 0.8.16.7

A CLI to work with DataHub metadata

Metadata Ingestion

This module hosts an extensible Python-based metadata ingestion system for DataHub. This supports sending data to DataHub using Kafka or through the REST API. It can be used through our CLI tool, with an orchestrator like Airflow, or as a library.

Getting Started

Prerequisites

Before running any metadata ingestion job, you should make sure that DataHub backend services are all running. If you are trying this out locally, the easiest way to do that is through quickstart Docker images.

Install from PyPI

The folks over at Acryl Data maintain a PyPI package for DataHub metadata ingestion.

# Requires Python 3.6+
python3 -m pip install --upgrade pip wheel setuptools
python3 -m pip install --upgrade acryl-datahub
datahub version
# If you see "command not found", try running this instead: python3 -m datahub version

If you run into an error, try checking the common setup issues.

Installing Plugins

We use a plugin architecture so that you can install only the dependencies you actually need. Click the plugin name to learn more about the specific source recipe and any FAQs!

Sources:

Plugin Name	Install Command	Provides
file	included by default	File source and sink
athena	pip install 'acryl-datahub[athena]'	AWS Athena source
bigquery	pip install 'acryl-datahub[bigquery]'	BigQuery source
bigquery-usage	pip install 'acryl-datahub[bigquery-usage]'	BigQuery usage statistics source
datahub-business-glossary	no additional dependencies	Business Glossary File source
dbt	no additional dependencies	dbt source
druid	pip install 'acryl-datahub[druid]'	Druid Source
feast	pip install 'acryl-datahub[feast]'	Feast source
glue	pip install 'acryl-datahub[glue]'	AWS Glue source
hive	pip install 'acryl-datahub[hive]'	Hive source
kafka	pip install 'acryl-datahub[kafka]'	Kafka source
kafka-connect	pip install 'acryl-datahub[kafka-connect]'	Kafka connect source
ldap	pip install 'acryl-datahub[ldap]' (extra requirements)	LDAP source
looker	pip install 'acryl-datahub[looker]'	Looker source
lookml	pip install 'acryl-datahub[lookml]'	LookML source, requires Python 3.7+
mongodb	pip install 'acryl-datahub[mongodb]'	MongoDB source
mssql	pip install 'acryl-datahub[mssql]'	SQL Server source
mysql	pip install 'acryl-datahub[mysql]'	MySQL source
mariadb	pip install 'acryl-datahub[mariadb]'	MariaDB source
openapi	pip install 'acryl-datahub[openapi]'	OpenApi Source
oracle	pip install 'acryl-datahub[oracle]'	Oracle source
postgres	pip install 'acryl-datahub[postgres]'	Postgres source
redash	pip install 'acryl-datahub[redash]'	Redash source
redshift	pip install 'acryl-datahub[redshift]'	Redshift source
sagemaker	pip install 'acryl-datahub[sagemaker]'	AWS SageMaker source
snowflake	pip install 'acryl-datahub[snowflake]'	Snowflake source
snowflake-usage	pip install 'acryl-datahub[snowflake-usage]'	Snowflake usage statistics source
sql-profiles	pip install 'acryl-datahub[sql-profiles]'	Data profiles for SQL-based systems
sqlalchemy	pip install 'acryl-datahub[sqlalchemy]'	Generic SQLAlchemy source
superset	pip install 'acryl-datahub[superset]'	Superset source
trino	pip install 'acryl-datahub[trino]	Trino source

Sinks

Plugin Name	Install Command	Provides
file	included by default	File source and sink
console	included by default	Console sink
datahub-rest	pip install 'acryl-datahub[datahub-rest]'	DataHub sink over REST API
datahub-kafka	pip install 'acryl-datahub[datahub-kafka]'	DataHub sink over Kafka

These plugins can be mixed and matched as desired. For example:

pip install 'acryl-datahub[bigquery,datahub-rest]'

You can check the active plugins:

datahub check plugins

Basic Usage

pip install 'acryl-datahub[datahub-rest]'  # install the required plugin
datahub ingest -c ./examples/recipes/example_to_datahub_rest.yml

Install using Docker

If you don't want to install locally, you can alternatively run metadata ingestion within a Docker container. We have prebuilt images available on Docker hub. All plugins will be installed and enabled automatically.

Limitation: the datahub_docker.sh convenience script assumes that the recipe and any input/output files are accessible in the current working directory or its subdirectories. Files outside the current working directory will not be found, and you'll need to invoke the Docker image directly.

# Assumes the DataHub repo is cloned locally.
./metadata-ingestion/scripts/datahub_docker.sh ingest -c ./examples/recipes/example_to_datahub_rest.yml

Install from source

If you'd like to install from source, see the developer guide.

Recipes

A recipe is a configuration file that tells our ingestion scripts where to pull data from (source) and where to put it (sink). Here's a simple example that pulls metadata from MSSQL and puts it into datahub.

# A sample recipe that pulls metadata from MSSQL and puts it into DataHub
# using the Rest API.
source:
  type: mssql
  config:
    username: sa
    password: ${MSSQL_PASSWORD}
    database: DemoData

transformers:
  - type: "fully-qualified-class-name-of-transformer"
    config:
      some_property: "some.value"

sink:
  type: "datahub-rest"
  config:
    server: "http://localhost:8080"

We automatically expand environment variables in the config, similar to variable substitution in GNU bash or in docker-compose files. For details, see https://docs.docker.com/compose/compose-file/compose-file-v2/#variable-substitution.

Running a recipe is quite easy.

datahub ingest -c ./examples/recipes/mssql_to_datahub.yml

A number of recipes are included in the examples/recipes directory. For full info and context on each source and sink, see the pages described in the table of plugins.

Transformations

If you'd like to modify data before it reaches the ingestion sinks – for instance, adding additional owners or tags – you can use a transformer to write your own module and integrate it with DataHub.

Check out the transformers guide for more info!

Using as a library

In some cases, you might want to construct the MetadataChangeEvents yourself but still use this framework to emit that metadata to DataHub. In this case, take a look at the emitter interfaces, which can easily be imported and called from your own code.

• DataHub emitter via REST (same requirements as datahub-rest). Basic usage example.
• DataHub emitter via Kafka (same requirements as datahub-kafka). Basic usage example.

Lineage with Airflow

There's a couple ways to get lineage information from Airflow into DataHub.

:::note

If you're simply looking to run ingestion on a schedule, take a look at these sample DAGs:

• generic_recipe_sample_dag.py - reads a DataHub ingestion recipe file and runs it
• mysql_sample_dag.py - runs a MySQL metadata ingestion pipeline using an inlined configuration.

:::

Using Datahub's Airflow lineage backend (recommended)

:::caution

The Airflow lineage backend is only supported in Airflow 1.10.15+ and 2.0.2+.

:::

Running on Docker locally

If you are looking to run Airflow and DataHub using docker locally, follow the guide here. Otherwise proceed to follow the instructions below.

Setting up Airflow to use DataHub as Lineage Backend

1. You need to install the required dependency in your airflow. See https://registry.astronomer.io/providers/datahub/modules/datahublineagebackend

  pip install acryl-datahub[airflow]

2. You must configure an Airflow hook for Datahub. We support both a Datahub REST hook and a Kafka-based hook, but you only need one.

   # For REST-based:
   airflow connections add  --conn-type 'datahub_rest' 'datahub_rest_default' --conn-host 'http://localhost:8080'
   # For Kafka-based (standard Kafka sink config can be passed via extras):
   airflow connections add  --conn-type 'datahub_kafka' 'datahub_kafka_default' --conn-host 'broker:9092' --conn-extra '{}'
   

3. Add the following lines to your airflow.cfg file.

   [lineage]
   backend = datahub_provider.lineage.datahub.DatahubLineageBackend
   datahub_kwargs = {
       "datahub_conn_id": "datahub_rest_default",
       "cluster": "prod",
       "capture_ownership_info": true,
       "capture_tags_info": true,
       "graceful_exceptions": true }
   # The above indentation is important!
   

   Configuration options:

   • datahub_conn_id (required): Usually datahub_rest_default or datahub_kafka_default, depending on what you named the connection in step 1.
   • cluster (defaults to "prod"): The "cluster" to associate Airflow DAGs and tasks with.
   • capture_ownership_info (defaults to true): If true, the owners field of the DAG will be capture as a DataHub corpuser.
   • capture_tags_info (defaults to true): If true, the tags field of the DAG will be captured as DataHub tags.
   • graceful_exceptions (defaults to true): If set to true, most runtime errors in the lineage backend will be suppressed and will not cause the overall task to fail. Note that configuration issues will still throw exceptions.

4. Configure inlets and outlets for your Airflow operators. For reference, look at the sample DAG in lineage_backend_demo.py, or reference lineage_backend_taskflow_demo.py if you're using the TaskFlow API.

5. [optional] Learn more about Airflow lineage, including shorthand notation and some automation.

Emitting lineage via a separate operator

Take a look at this sample DAG:

• lineage_emission_dag.py - emits lineage using the DatahubEmitterOperator.

In order to use this example, you must first configure the Datahub hook. Like in ingestion, we support a Datahub REST hook and a Kafka-based hook. See step 1 above for details.

Developing

See the guides on developing, adding a source and using transformers.