Difference between DataRaptor Turbo Extract and DataRaptor Extract

Overview

When building integrations and data transforms in Salesforce Omnistudio (Vlocity), choosing the right DataRaptor extract method impacts performance, payload size, and supported features. This article explains the differences between DataRaptor Turbo Extract and the classic DataRaptor Extract, covering use-cases, performance characteristics, limitations, and best practices.

Key Differences at a Glance

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Performance: Turbo Extract is optimized for speed and lower latency, especially for simple, shallow data retrievals. Classic Extract is more feature-rich but can be slower for large or complex mappings.
Payload and Mapping: Turbo Extract returns raw JSON from the data service (faster) and expects minimal post-processing; classic Extract supports complex mappings, output templates, and richer transforms.
Supported Features: Classic Extract supports formulas, datamasks, nested output templates, and complex merge behavior. Turbo Extract is limited and does not support all transformation features.
Use-cases: Use Turbo Extract for high-throughput, read-only scenarios where you need simple field selections. Use classic Extract when you need mapping, transformation, or multi-source joins.

Detailed Comparison

1. Architecture & How They Work

DataRaptor Extract (classic) processes ETL-like mappings. It can:

Map fields from source to complex nested output structures
Apply transformations like formulas, static values, and lookups
Use output templates to shape the JSON structure returned to the client

DataRaptor Turbo Extract is a lightweight extractor that bypasses heavier mapping logic and returns the dataset quickly, often as the raw response from the underlying data source (Salesforce or Integration Procedures). Turbo is implemented to reduce CPU cycles and serialization overhead.

2. Performance & Scalability

Turbo Extract is faster because it:

Skips expensive mapping and transformation steps
Minimizes memory allocation and JSON transformations
Is ideal for large record sets when you only need a subset of fields

Classic Extract can be slower but gives control when you need complex outputs. If performance is critical and the extract is simple (no complex output template), Turbo Extract is preferred.

3. Feature Support

Classic DataRaptor supports:

Output templates and nested JSON structures
Formulas and value mapping
Data masking and conditional logic
Multiple data sources (via Integration Procedures) and merges

Turbo Extract limitations:

No advanced mapping or output templating — returns data as-is
Limited or no support for DataRaptor formulas and datamasks
Best for single-source, direct field extraction

4. Typical Use Cases

Use Turbo Extract when:

You need a quick list of records or related records with a fixed set of fields
Latency and throughput are top priorities (e.g., high-traffic UI components)
No complex transformation or nested JSON shaping is required

Use Classic Extract when:

Output needs nested structures, calculated fields, or formatted values
You need to combine or merge multiple data sources before returning the payload
Business logic requires DataRaptor transforms

5. Example: When to Choose Which

Scenario A — Account list in a lightning component: You need only Account Name, Id, and Industry for 1000+ rows. Turbo Extract is ideal for performance.

Scenario B — Customer summary payload with nested contacts, address formatting, and calculated metrics: Classic DataRaptor Extract is appropriate.

6. Sample Turbo Extract Result (JSON)

{
  "records": [
    {"Id": "001xxxx", "Name": "Acme Corp", "Industry": "Manufacturing"},
    {"Id": "001yyyy", "Name": "Blue Inc", "Industry": "Finance"}
  ],
  "totalSize": 2
}

Note: Classic Extract would allow you to map this into a nested format, add computed fields, or mask sensitive data before returning.

Best Practices

Start with Turbo Extract for read-heavy, simple-field scenarios and benchmark latency.
Use Classic Extract when you need output templating, formulas, or combining multiple sources.
Measure response times and memory usage. If Classic Extract becomes a bottleneck, consider moving heavy transforms server-side or into Integration Procedures.
Keep security in mind — apply datamasks and access checks where needed (classic supports these features).

Conclusion

DataRaptor Turbo Extract and DataRaptor Extract solve different needs: Turbo focuses on speed and lightweight retrieval, while classic Extract provides full-featured mapping and transformation. Choose Turbo for performance-sensitive, simple extracts and classic Extract when you need complex shaping, formulas, or multi-source merging.