A CLI to work with DataHub metadata
Project description
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 |
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 |
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 itmysql_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
- You need to install the required dependency in your airflow. See https://registry.astronomer.io/providers/datahub/modules/datahublineagebackend
pip install acryl-datahub[airflow]
-
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 '{}'
-
Add the following lines to your
airflow.cfg
file.[lineage] backend = datahub_provider.lineage.datahub.DatahubLineageBackend datahub_kwargs = { "datahub_conn_id": "datahub_rest_default", "capture_ownership_info": true, "capture_tags_info": true, "graceful_exceptions": true } # The above indentation is important!
Configuration options:
datahub_conn_id
(required): Usuallydatahub_rest_default
ordatahub_kafka_default
, depending on what you named the connection in step 1.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.
-
Configure
inlets
andoutlets
for your Airflow operators. For reference, look at the sample DAG inlineage_backend_demo.py
, or referencelineage_backend_taskflow_demo.py
if you're using the TaskFlow API. -
[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.
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