pingpong-datahub 0.8.26

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+
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
tableau	pip install 'acryl-datahub[tableau]'	Tableau 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.