acryl-datahub 1.5.0.13.post3

A CLI to work with DataHub metadata

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:

• Developing on Metadata Ingestion
• Adding a Metadata Ingestion Source
• Using Transformers