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1.4.0.9.post2

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acryl-datahub-hcc-patched

A CLI to work with DataHub metadata

Published15 Apr 2026
Direct dependencies203
Transitive dependenciesNot calculated

README

Version 1.4.0.9.post2 documentation

Introduction to Metadata Ingestion

:::tip Find Integration Source Please see our Integrations page to browse our ingestion sources and filter on their features. :::

Integration Methods

DataHub offers three methods for data ingestion:

  • UI Ingestion : Easily configure and execute a metadata ingestion pipeline through the UI.
  • CLI Ingestion guide : Configure the ingestion pipeline using YAML and execute by it through CLI.
  • SDK-based ingestion : Use Python Emitter or Java emitter to programmatically control the ingestion pipelines.

Types of Integration

Integration can be divided into two concepts based on the method:

Push-based Integration

Push-based integrations allow you to emit metadata directly from your data systems when metadata changes. Examples of push-based integrations include Airflow, Spark, Great Expectations and Protobuf Schemas. This allows you to get low-latency metadata integration from the "active" agents in your data ecosystem.

Pull-based Integration

Pull-based integrations allow you to "crawl" or "ingest" metadata from the data systems by connecting to them and extracting metadata in a batch or incremental-batch manner. Examples of pull-based integrations include BigQuery, Snowflake, Looker, Tableau and many others.

Core Concepts

The following are the core concepts related to ingestion:

  • Sources: Data systems from which extract metadata. (e.g. BigQuery, MySQL)
  • Sinks: Destination for metadata (e.g. File, DataHub)
  • Recipe: The main configuration for ingestion in the form or .yaml file

For more advanced guides, please refer to the following:

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  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
more-itertools<11.0.0,>=8.12.0; extra == "bigquery"runtime
numpy
Environment
{
  "extras": "",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
numpy<2; extra == "bigquery"runtime
parse
Environment
{
  "extras": "",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
parse<2.0.0,>=1.19.0; extra == "bigquery"runtime
patchy
Environment
{
  "extras": "",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
patchy==2.8.0; extra == "bigquery"runtime
pip
Environment
{
  "extras": "",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
pip; extra == "bigquery"runtime
protobuf
Environment
{
  "extras": "",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
protobuf<7.0.0,>=5.0.0; extra == "bigquery"runtime
schwifty
Environment
{
  "extras": "",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
schwifty<2026.0.0; extra == "bigquery"runtime
scipy
Environment
{
  "extras": "",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
scipy<2.0.0,>=1.7.2; extra == "bigquery"runtime
sqlalchemy-bigquery
Environment
{
  "extras": "",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
sqlalchemy-bigquery<2.0.0,>=1.4.1; extra == "bigquery"runtime
sqlalchemy
Environment
{
  "extras": "",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
sqlalchemy<2,>=1.4.39; extra == "bigquery"runtime
sqlglot
Environment
{
  "extras": "c",
  "marker": "extra == \"bigquery\"",
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
sqlglot[c]==29.0.1; extra == "bigquery"runtime

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acryl_datahub_hcc_patched-1.4.0.9.post2-py3-none-any.whl

bdist_wheel · 2,707.31 KB · Python py3

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