This package allows to generate documentation of data pipelines and data lineage charts. It is source agnostic and uses a predefined json/yaml format to represent the dependencies and business logic. The resulting markdown files can be used standalone or as part of a documentation site using tools like MkDocs or VuePress.
npm install -g @datayoga-io/lineage
To quickly get started with Lineage, scaffold a new project. This will create the folder structure along with sample files.
dy-lineage scaffold ./my-project
To generate the documentation for the new project:
dy-lineage build ./my-project --dest ./docs
Lineage models the data ecosystem using the following entities:
Datastore – A datastore represents a source or target of data that can hold data at rest or data in motion. Datastores include entities such as a table in a database, a file, or a stream in Kafka. A Datastore can act either as a source or a target of a pipeline.
File – A file is a type of Datastore that represents information stored in files. Files contain metadata about their structure and schema.
Dimension – A dimension table / file is typically used for lookup and constant information that is managed as part of the application code. This often includes lookup values such as country codes.
Runner – A runner is a processing engine capable of running data operations. Every Runner supports one or more programming languages. Some Runners, like a database engine, only support SQL, while others like Spark may support Python, Scala, and Java.
Consumer – A consumer consumes data and presents it to a user. Consumers include reports, dashboards, and interactive applications.
Pipeline – A pipeline represents a series of
Jobs that operate on a single
Job – A job is composed of a series of Steps that fetch information from one or more Datastores, transform them, and store the result in a target Datastore, or perform actions such as sending out alerts or performing HTTP calls.
Job Step – Every step in a job performs a single action. A step can be of a certain type representing the action it performs. A step can be an SQL statement, a Python statement, or a callout to a library. Steps can be chained to create a Directed Acyclic Graph (DAG).
See the Example folder for sample input files and generated output files
Structure of input folder
. ├── .dyrc ├── datastores ├── files ├── pipelines ├── relations
.dyrc: Used to store global configuration.
datastores: Catalog file(s) with information about datastore entities and their metadata
files: Catalog file(s) with information about file datastore entities and their metadata
pipelines: Optional files containing information about the pipelines and business logic flow
relations: Information about the relations between the data entities
Structure of catalog file
The catalog file contains the entity definition and metadata for each of the entities.
The node naming convention is:
. Module name can be nested: e.g.
pipeline:order_mgmt.load_orders: datastore:orders: datastore:raw_orders:
Structure of relations file
The relations file holds the relationships between the entities.
- source: datastore:raw_orders target: pipeline:order_mgmt.load_orders - source: pipeline:order_mgmt.load_orders target: datastore:orders
Adding metadata and business logic flow to pipelines
Lineage collectors enable to export lineage knowlege from external systems to be processed and documented.