This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 1.7.0 instead.
Reason given by maintainers: Rest emitter has a bug. Fix coming in 0.8.25.2
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+ |
| metabase | pip install 'acryl-datahub[metabase] |
Metabase source |
| mode | pip install 'acryl-datahub[mode]' |
Mode Analytics source |
| 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 |
| starburst-trino-usage | pip install 'acryl-datahub[starburst-trino-usage]' |
Starburst Trino usage statistics source |
| nifi | pip install 'acryl-datahub[nifi] |
Nifi 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
The --dry-run option of the ingest command performs all of the ingestion steps, except writing to the sink. This is useful to ensure that the
ingestion recipe is producing the desired workunits before ingesting them into datahub.
# Dry run
datahub ingest -c ./examples/recipes/example_to_datahub_rest.yml --dry-run
# Short-form
datahub ingest -c ./examples/recipes/example_to_datahub_rest.yml -n
The --preview option of the ingest command performs all of the ingestion steps, but limits the processing to only the first 10 workunits produced by the source.
This option helps with quick end-to-end smoke testing of the ingestion recipe.
# Preview
datahub ingest -c ./examples/recipes/example_to_datahub_rest.yml --preview
# Preview with dry-run
datahub ingest -c ./examples/recipes/example_to_datahub_rest.yml -n --preview
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"
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.
Handling sensitive information in recipes
We automatically expand environment variables in the config (e.g. ${MSSQL_PASSWORD}),
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. This environment variable substitution should be used to mask sensitive information in recipe files. As long as you can get env variables securely to the ingestion process there would not be any need to store sensitive information in recipes.
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 Metadata events directly and use programmatic ways to emit that metadata to DataHub. In this case, take a look at the Python emitter and the Java emitter libraries which can be called from your own code.
Programmatic Pipeline
In some cases, you might want to configure and run a pipeline entirely from within your custom python script. Here is an example of how to do it.
- programmatic_pipeline.py - a basic mysql to REST programmatic pipeline.
Developing
See the guides on developing, adding a source and using transformers.
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