ddeutil-workflow 0.0.3

Data Developer & Engineer Workflow Utility Objects

Data Utility: Workflow

Table of Contents:

• Installation
• Getting Started
  • Connection
  • Dataset
  • Schedule
• Examples
  • Python
  • Tasks (EL)
  • Hooks (T)

This Utility Workflow objects was created for easy to make a simple metadata driven pipeline that able to ETL, T, EL, or ELT by .yaml file.

I think we should not create the multiple pipeline per use-case if we able to write some dynamic pipeline that just change the input parameters per use-case instead. This way we can handle a lot of pipelines in our orgs with metadata only. It called Metadata Driven.

Next, we should get some monitoring tools for manage logging that return from pipeline running. Because it not show us what is a use-case that running data pipeline.

[!NOTE] Disclaimer: I inspire the dynamic statement from the GitHub Action .yml files and all of config file from several data orchestration framework tools from my experience on Data Engineer.

Installation

pip install ddeutil-workflow

This project need ddeutil-io, ddeutil-model extension namespace packages.

Getting Started

The first step, you should start create the connections and datasets for In and Out of you data that want to use in pipeline of workflow. Some of this component is similar component of the Airflow because I like it concepts.

The main feature of this project is the Pipeline object that can call any registries function. The pipeline can handle everything that you want to do, it will passing parameters and catching the output for re-use it to next step.

[!IMPORTANT] In the future of this project, I will drop the connection and dataset to dynamic registries instead of main features because it have a lot of maintain vendor codes and deps. (I do not have time to handle this features)

Connection

The connection for worker able to do any thing.

conn_postgres_data:
  type: conn.Postgres
  url: 'postgres//username:${ENV_PASS}@hostname:port/database?echo=True&time_out=10'

from ddeutil.workflow.conn import Conn

conn = Conn.from_loader(name='conn_postgres_data', externals={})
assert conn.ping()

Dataset

The dataset is define any objects on the connection. This feature was implemented on /vendors because it has a lot of tools that can interact with any data systems in the data tool stacks.

ds_postgres_customer_tbl:
  type: dataset.PostgresTbl
  conn: 'conn_postgres_data'
  features:
    id: serial primary key
    name: varchar( 100 ) not null

from ddeutil.workflow.vendors.pg import PostgresTbl

dataset = PostgresTbl.from_loader(name='ds_postgres_customer_tbl', externals={})
assert dataset.exists()

Schedule

schd_for_node:
  type: schedule.Schedule
  cron: "*/5 * * * *"

from ddeutil.workflow.schedule import Schedule

scdl = Schedule.from_loader(name='schd_for_node', externals={})
assert '*/5 * * * *' == str(scdl.cronjob)

cron_iterate = scdl.generate('2022-01-01 00:00:00')
assert '2022-01-01 00:05:00' f"{cron_iterate.next:%Y-%m-%d %H:%M:%S}"
assert '2022-01-01 00:10:00' f"{cron_iterate.next:%Y-%m-%d %H:%M:%S}"
assert '2022-01-01 00:15:00' f"{cron_iterate.next:%Y-%m-%d %H:%M:%S}"
assert '2022-01-01 00:20:00' f"{cron_iterate.next:%Y-%m-%d %H:%M:%S}"
assert '2022-01-01 00:25:00' f"{cron_iterate.next:%Y-%m-%d %H:%M:%S}"

Examples

This is examples that use workflow file for running common Data Engineering use-case.

Python

The state of doing lists that worker should to do. It be collection of the stage.

run_py_local:
  type: ddeutil.workflow.pipe.Pipeline
  params:
    author-run:
      type: str
    run-date:
      type: datetime
  jobs:
    first-job:
      stages:
        - name: Printing Information
          id: define-func
          run: |
            x = '${{ params.author-run }}'
            print(f'Hello {x}')

            def echo(name: str):
              print(f'Hello {name}')

        - name: Run Sequence and use var from Above
          vars:
            x: ${{ params.author-run }}
          run: |
            print(f'Receive x from above with {x}')
            # Change x value
            x: int = 1

        - name: Call Function
          vars:
            echo: ${{ stages.define-func.outputs.echo }}
          run: |
            echo('Caller')

from ddeutil.workflow.pipeline import Pipeline

pipe = Pipeline.from_loader(name='run_py_local', externals={})
pipe.execute(params={'author-run': 'Local Workflow', 'run-date': '2024-01-01'})

> Hello Local Workflow
> Receive x from above with Local Workflow
> Hello Caller

Tasks (Extract & Load)

pipe_el_pg_to_lake:
  type: ddeutil.workflow.pipe.Pipeline
  params:
    run-date:
      type: datetime
    author-email:
      type: str
  jobs:
    extract-load:
      stages:
        - name: "Extract Load from Postgres to Lake"
          id: extract-load
          task: tasks/postgres-to-delta@polars
          with:
            source:
              conn: conn_postgres_url
              query: |
                select * from ${{ params.name }}
                where update_date = '${{ params.datetime }}'
            sink:
              conn: conn_az_lake
              endpoint: "/${{ params.name }}"

Tasks (Transform)

pipe_hook_mssql_proc:
  type: ddeutil.workflow.pipe.Pipeline
  params:
    run_date: datetime
    sp_name: str
    source_name: str
    target_name: str
  jobs:
    transform:
      stages:
        - name: "Transform Data in MS SQL Server"
          id: transform
          task: tasks/mssql-proc@odbc
          with:
            exec: ${{ params.sp_name }}
            params:
              run_mode: "T"
              run_date: ${{ params.run_date }}
              source: ${{ params.source_name }}
              target: ${{ params.target_name }}

License

This project was licensed under the terms of the MIT license.