DAZL Documentation | Data Analytics A-to-Z Processing Language

loadInline

data management

slug: step-loadinline

Purpose

Loads data directly from inline definitions into the workflow pipeline. This step is ideal for small datasets, testing, prototyping, or when you want to define data explicitly within the workflow YAML.

When to Use

Rapidly test workflow logic without external datasets
Provide small sample datasets for demonstrations or prototypes
Inline initialization for charts, summaries, or frequency steps
Seed workflows with predefined static data

How It Works

Receives an array of records directly via the data parameter in the workflow YAML.
Wraps the provided dataset into the standard pipeline structure (data, pdv, extras).
Returns the dataset as an in-memory object that downstream steps can reference.
Tracks basic metadata such as the number of records loaded.

Parameters

Required

data (array) — Array of associative arrays representing dataset records. Each record is a map of field names to values.

Optional

output (string) — Alias for referencing the dataset in later workflow steps.

Security Features

Only operates on data explicitly provided in the workflow YAML.
No external data access, evaluation, or execution is performed.

Input Requirements

data must be a valid array of associative arrays.
Each record should have consistent fields to ensure proper handling by downstream steps.

Output

Data

Returns the provided inline dataset as-is.

PDV

Preserves any existing metadata (pdv) from the pipeline input.

Extras

Includes a record_count indicating the number of rows loaded.

Output Structure

Key	Description
`data`	Array of dataset records
`pdv`	Metadata about columns (passed through from input)
`extras`	Record count and optional diagnostics
`outputType`	`"work"` — signals an in-memory dataset

Example Usage

steps:
  - loadInline:
      data:
        # Young Segment
        - {age: 22, income: 38000, spend: 800,  segment: 'Young'}
        - {age: 25, income: 45000, spend: 1200, segment: 'Young'}
        - {age: 29, income: 56000, spend: 1800, segment: 'Young'}
        - {age: 31, income: 60000, spend: 2000, segment: 'Young'}

        # Mid Segment
        - {age: 34, income: 67000, spend: 2100, segment: 'Mid'}
        - {age: 38, income: 74000, spend: 2600, segment: 'Mid'}
        - {age: 41, income: 79000, spend: 2800, segment: 'Mid'}
        - {age: 44, income: 83000, spend: 3000, segment: 'Mid'}

        # Senior Segment
        - {age: 46, income: 87000, spend: 3100, segment: 'Senior'}
        - {age: 50, income: 95000, spend: 3700, segment: 'Senior'}
        - {age: 55, income: 102000, spend: 4200, segment: 'Senior'}
        - {age: 60, income: 110000, spend: 4800, segment: 'Senior'}

      output: testData

  - chart:
      dataset: testData
      type: bubble
      x_axis: income
      y_axis: spend
      z_axis: age
      series: segment

Example Output

{
  "data": [
    {"age":22,"income":38000,"spend":800,"segment":"Young"},
    {"age":25,"income":45000,"spend":1200,"segment":"Young"},
    {"age":29,"income":56000,"spend":1800,"segment":"Young"},
    {"age":31,"income":60000,"spend":2000,"segment":"Young"},
    {"age":34,"income":67000,"spend":2100,"segment":"Mid"},
    {"age":38,"income":74000,"spend":2600,"segment":"Mid"},
    {"age":41,"income":79000,"spend":2800,"segment":"Mid"},
    {"age":44,"income":83000,"spend":3000,"segment":"Mid"},
    {"age":46,"income":87000,"spend":3100,"segment":"Senior"},
    {"age":50,"income":95000,"spend":3700,"segment":"Senior"},
    {"age":55,"income":102000,"spend":4200,"segment":"Senior"},
    {"age":60,"income":110000,"spend":4800,"segment":"Senior"}
  ],
  "pdv": {},
  "extras": {"record_count":12},
  "outputType": "work"
}

DAZL Documentation | Data Analytics A-to-Z Processing Language

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