Core Concepts

Understanding these foundational concepts will help you make the most of Lume’s capabilities. This guide introduces the key components and how they work together.

Project Basics

Source Data

The input data you want to transform

Target Schema

The desired structure for your output data

Source Data

Source data is any user-provided data that you want to interpret or transform. Lume currently supports CSV. On the roadmap, we plan to support the following:

• Excel
• XML
• And more

Example Source Data

id,name
0018y000008hFqqAAE,Blue Sky Ventures LLC
0018y000008nrMrAAI,Green Acres Holdings LLC

While Lume only requires a single record to generate mapping logic, providing larger data samples improves mapping accuracy through better pattern recognition.

Need support for additional data formats? Contact the Lume team for assistance!

Target Schema

A target schema defines the desired output format for your transformed data. It uses YAML format to specify:

• Target Model Name
• Column Names
• Test Rules
• Business logic and descriptons

Remember: Property names in Lume’s API cannot contain periods (.).

Example Target Schema

models:
- name: customers
  description: "Customer records and metadata"
  columns:
    - name: customer_id
      description: "Unique identifier for each customer"
      tests:
        - not_null
        - unique

    - name: customer_name
      description: "Full legal name of the customer"
      tests:
        - not_null

    - name: customer_type
      description: "Type of customer (e.g., individual, business)"
      tests:
        - accepted_values:
            values: ["individual", "business"]

- name: orders
  description: "All customer orders"
  columns:
    - name: order_id
      description: "Primary key for the order"
      tests:
        - not_null
        - unique

    - name: customer_id
      description: "Foreign key to customers"
      tests:
        - not_null
        - relationships:
            to: ref('customers')
            field: customer_id

    - name: status
      description: "Current status of the order"
      tests:
        - accepted_values:
            values: ["pending", "shipped", "delivered", "cancelled"]

- name: payments
  description: "Payments made toward orders"
  columns:
    - name: payment_id
      description: "Unique ID for the payment record"
      tests:
        - not_null
        - unique

    - name: order_id
      description: "Associated order ID"
      tests:
        - relationships:
            to: ref('orders')
            field: order_id

    - name: payment_method
      description: "Method of payment (e.g., credit card, PayPal)"
      tests:
        - accepted_values:
            values: ["credit_card", "paypal", "bank_transfer"]

Key Components

Projects

Orchestrate your data transformation journey

File Manager

Manage files for a given project

Workbook

AI-powered data transformation

Code

Execute and monitor your transformations

Lineage

Track table and column lineage

Data

Easily view the transformed data

Projects

A Project is your complete data transformation pipeline. It can:

• Accept multiple file inputs (sources and seeds)
• Include multiple transformation steps
• Join and combine data
• Produce final mapped output

Projects help you organize related transformations into logical sequences. Complex transformations can be broken down into manageable steps, making them easier to maintain and modify.

File Manager

The file manager is a place to manage and access your uploaded data. It can:

• View metadata per model on row count, column count, and file size.
• Insert, upsert, and remove source tables and source seed files.
• Add additional context to the source table description to guide the AI generation.
• Provide column level metadata around data type, nullability, and additional notes.

Workbook

Lume generates a spreadsheet style artifact called a Workbook, but you don’t need to be a programmer to use it effectively. The platform provides:

Core Concepts:

• Data lineage showing how fields map between source and target
• Sample data previews for curosry visual inspections
• Natural language explanations of the transformation logic
• Interactive edit interface for adjusting or providing additional mapping context
• AI Chat to explore daata nd gain a deeper understanding

Code

Code represents the section to gain insights about the testing validation and sql models produced:

• Compiled Code
• Data Preview
• Lineage
• Validation

Lineage

A visual representation of the table and column level lineage to better understand the relationships between the transformations that Lume’s AI engine created.

Data

Lume provides comprehensive target data review. You can quickly scan the set of produced data to ensure it passes a quick visual inspection.