Frequently Asked Questions

Getting Started

What is Lume?

Lume is an AI-powered data mapping platform that automates the process of transforming data between different schemas. It helps you:

• Normalize data from multiple sources
• Ingest client data automatically
• Create and maintain data pipelines
• Map data in minutes rather than days or weeks

How do I get started with Lume?

1. Contact Lume to set up your account and get API keys
2. Install the SDK for your preferred language (Python or TypeScript)
3. Define your target schema (or upload a sample CSV)
4. Start mapping your data

Check out our Quickstart Guide for detailed steps.

Security & Compliance

Is Lume secure?

Yes, Lume is SOC 2 Type 1 and Type 2 compliant. We implement comprehensive security measures including:

• Data encryption at rest and in transit
• Multi-factor authentication
• Least-privilege access control
• Regular penetration testing
• Vulnerability scanning
• Secure data retention and disposal procedures

How does Lume handle data privacy?

Lume follows strict data privacy principles:

• Customer data is purged when service is terminated
• Formal data retention procedures are in place
• All data transmission is encrypted
• Access to data is strictly controlled
• Regular security training for all employees

Technical Details

What programming languages do you support?

Lume currently provides SDKs for:

• TypeScript/JavaScript
• Python

Both SDKs are currently in beta. Contact support for the latest versions and features.

What source data formats do you support?

Lume currently supports:

• JSON
• CSV
• Excel

Contact us if you need support for additional formats.

What target schema format do you use?

Target schemas conform to the JSON Schema protocol. This allows you to specify:

• Data types
• Field requirements
• Validation rules
• Format specifications
• Nested structures

How long does it take to generate mapping logic?

Generation time varies from seconds to several minutes, depending on schema complexity:

• Simple schemas: A few seconds
• Complex schemas: Up to 20 minutes

Note: This is only for initial generation. Subsequent runs using existing pipelines execute immediately.

Why does Lume require sample source data and not the source schema itself?

1. Lume infers the schema from the data sample.
2. Lume uses the values of source data to create a more holistic semantic understanding of your source key definition and your source data as a whole. This helps increase mapping accuracy significantly.

How does the AI leverage the source sample?

Lume recreates the human process of mapping data. In this step, it is common that the source keys are not semantically meaningful. Namely, key names not be related to what the data is actually storing. Thus, it is common to analyze the data to understand what the property truly is storing. Lume goes through this understanding process via its AI.

Troubleshooting

What should I do if I encounter mapping errors?

Lume provides detailed validation error information including:

• Error type and location
• Error statistics
• Sample problematic values
• Schema path information

Review the Validation Errors guide for detailed troubleshooting steps.

How can I improve mapping accuracy?

To improve mapping accuracy:

1. Provide clear, detailed descriptions in your target schema
2. Use full phrases rather than single words
3. Include business context and edge cases
4. Provide larger data samples when possible
5. Use the schema validation features to enforce data quality

Usability

Why doesn't my file download start when I click the download button?

If you’re experiencing issues with file downloads not starting, this may be due to your browser’s pop-up blocker settings. To resolve this:

1. Check if your browser is blocking pop-ups for the Lume application
2. Temporarily disable the pop-up blocker for the Lume domain
3. Try downloading the file again

This should resolve the issue in most cases. If the problem persists, please contact our support team.