DataYoga Core

Introduction

datayoga-core is the transformation engine used in DataYoga, a framework for building and generating data pipelines.

Installation

pip install datayoga-core

Quick Start

This demonstrates how to transform data using a DataYoga job.

Create a Job

Use this example.dy.yaml:

steps:
  - uses: add_field
    with:
      fields:
        - field: full_name
          language: jmespath
          expression: concat([fname, ' ' , lname])
        - field: country
          language: sql
          expression: country_code || ' - ' || UPPER(country_name)
  - uses: rename_field
    with:
      fields:
        - from_field: fname
          to_field: first_name
        - from_field: lname
          to_field: last_name
  - uses: remove_field
    with:
      fields:
        - field: credit_card
        - field: country_name
        - field: country_code
  - uses: map
    with:
      expression:
        {
          first_name: first_name,
          last_name: last_name,
          greeting: "'Hello ' || CASE WHEN gender = 'F' THEN 'Ms.' WHEN gender = 'M' THEN 'Mr.' ELSE 'N/A' END || ' ' || full_name",
          country: country,
          full_name: full_name
        }
      language: sql

Transform Data Using datayoga-core

Use this code snippet to transform a data record using the job defined above. The transform method returns a tuple of processed, filtered, and rejected records:

import datayoga_core as dy
from datayoga_core.job import Job
from datayoga_core.result import Result, Status
from datayoga_core.utils import read_yaml

job_settings = read_yaml("example.dy.yaml")
job = dy.compile(job_settings)

assert job.transform([{"fname": "jane", "lname": "smith", "country_code": 1, "country_name": "usa", "credit_card": "1234-5678-0000-9999", "gender": "F"}]).processed == [
  Result(status=Status.SUCCESS, payload={"first_name": "jane", "last_name": "smith", "country": "1 - USA", "full_name": "jane smith", "greeting": "Hello Ms. jane smith"})]

The job can also be provided as a parsed json inline:

import datayoga_core as dy
from datayoga_core.job import Job
from datayoga_core.result import Result, Status
import yaml
import textwrap

job_settings = textwrap.dedent("""
  steps:
    - uses: add_field
      with:
        fields:
          - field: full_name
            language: jmespath
            expression: concat([fname, ' ' , lname])
          - field: country
            language: sql
            expression: country_code || ' - ' || UPPER(country_name)
    - uses: rename_field
      with:
        fields:
          - from_field: fname
            to_field: first_name
          - from_field: lname
            to_field: last_name
    - uses: remove_field
      with:
        fields:
          - field: credit_card
          - field: country_name
          - field: country_code
    - uses: map
      with:
        expression:
          {
            first_name: first_name,
            last_name: last_name,
            greeting: "'Hello ' || CASE WHEN gender = 'F' THEN 'Ms.' WHEN gender = 'M' THEN 'Mr.' ELSE 'N/A' END || ' ' || full_name",
            country: country,
            full_name: full_name
          }
        language: sql
""")
job = dy.compile(yaml.safe_load(job_settings))

assert job.transform([{"fname": "jane", "lname": "smith", "country_code": 1, "country_name": "usa", "credit_card": "1234-5678-0000-9999", "gender": "F"}]).processed == [
  Result(status=Status.SUCCESS, payload={"first_name": "jane", "last_name": "smith", "country": "1 - USA", "full_name": "jane smith", "greeting": "Hello Ms. jane smith"})]

As can be seen, the record has been transformed based on the job:

• fname field renamed to first_name.
• lname field renamed to last_name.
• country field added based on an SQL expression.
• full_name field added based on a JMESPath expression.
• greeting field added based on an SQL expression.

Examples

• Add a new field country out of an SQL expression that concatenates country_code and country_name fields after upper case the later:

  uses: add_field
  with:
    field: country
    language: sql
    expression: country_code || ' - ' || UPPER(country_name)
  

• Rename fname field to first_name and lname field to last_name:

  uses: rename_field
  with:
    fields:
      - from_field: fname
        to_field: first_name
      - from_field: lname
        to_field: last_name
  

• Remove credit_card field:

  uses: remove_field
  with:
    field: credit_card
  

For a full list of supported block types see reference.

Expression Language

DataYoga supports both SQL and JMESPath expressions. JMESPath are especially useful to handle nested JSON data, while SQL is more suited to flat row-like structures.

For more information about custom functions and supported expression language syntax see reference.