AML Dataset

Knowledge Checkpoint

A grasp of these concepts will help you understand this documentation better:

• Dataset

Introduction

In Holistics, datasets are defined in .dataset.aml files. The full dataset file name has the form dataset_name.dataset.aml. The dataset definition typically contains the following information:

• Dataset metadata: dataset labels, descriptions, owners
• Data Source reference: users' exploration activities will use this source
• Data models included
• Relationships
• Metrics
• Dataset view definition

The following section will list all current dataset parameters.

Parameter definition

Parameter name	Description
import	Add other files to the current dataset file (Deprecated in AML 2.0)
dataset	Specify the dataset's unique name in the workspace
label	Specifies how the dataset will appear in the Ready-to-explore Dataset
description	Add dataset description
owner	Define who should be in charge of managing the current dataset
data_source_name	Specify the database that Holistics will execute the generated query against (in the dataset)
relationships	Specify relationship and their configuration among added models
models	Specify which models will be used in the dataset
view	Define how models and fields are displayed in Preview / Dataset Exploration
dimension	Define cross-model dimensions in the dataset
metric	Define metrics to be used in the dataset

Dataset syntax example

Below is a sample dataset with all parameters filled out:

Dataset simple_dataset {
  label: '[Demo] Ecommerce'
  description: 'Demo dataset for E-commerce use cases test'
  owner: '[email protected]'
  data_source_name: 'demodb'

  models: [
    ecommerce_orders,
    ecommerce_order_items,
    ecommerce_users,
    ecommerce_products,
    ecommerce_categories
  ]

  relationships: [
    relationship(ecommerce_orders.user_id > ecommerce_users.id, true),
    relationship(ecommerce_order_items.order_id > ecommerce_orders.id, true),
    relationship(ecommerce_order_items.product_id > ecommerce_products.id, true),
    relationship(ecommerce_products.category_id > ecommerce_categories.id, true)
  ]

  // Dimensions definition
  dimension full_name {
    model: users 
    type: 'text'
    label: 'Full name'
    definition: @aql concat(users.first_name, ' ', users.last_name);;
  }

  dimension age_by_year {
    model: users 
    type: 'text'
    label: 'Full name'
    definition: @aql date_diff('day', users.birth_date, @now) / 365;;
  }  

  // Metrics definition
  metric count_orders {
    label: 'Count Orders'
    type: 'number'
    definition: @aql ecommerce_orders.id | count() ;;
  }

  metric sum_order_value {
    label: 'Sum Order Values'
    type: 'number'
    definition: @aql ecommerce_order_items | sum(ecommerce_order_items.quantity * ecommerce_products.price) ;;
  }

  metric average_order_value {
    label: 'Average Order Value'
    type: 'number'
    definition: @aql sum_order_value / count_orders;;
  }

  // View definition
  view {
    model ecommerce_orders { }
    model ecommerce_users { }

    // Create group "Order Master" containing two models Orders and Dates 
    group relevant_models {
      model ecommerce_products { }
      model ecommerce_categories { }
    }

    group business_metrics {
      metric sum_order_value
      metric average_order_value
    }
  }
}

Example dataset UI