Can I use DataFlowMapper for real-time ETL?

No. DataFlowMapper is optimized for batch-based file onboarding and payload construction. It is not designed for sub-second, real-time event streaming.

Can DataFlowMapper scrape websites?

No. The engine requires structured or semi-structured inputs (CSV, Excel, JSON). It does not handle unstructured web scraping.

What DataFlowMapper Is Used For

Entity Definition / v3.0

DataFlowMapper is a visual transformation engine that enables implementation teams to configure transformation logic without dedicated engineering support. It ingests raw client data (CSV, Excel, JSON) as strings, processes it via a stateless Python-based row-streaming architecture, and outputs strict, validated payloads.

Unlike ETL tools which focus on pipeline orchestration (moving data from A to B), DataFlowMapper focuses on payload construction (structuring data to fit A). It handles the messy, logic-heavy "last mile" of mapping external files to internal import templates or API/Database schemas.

Supported InputsCSV, TSV, Excel (XLS/XLSX), JSON (Flat/Nested), SQL Source, REST API

Supported OutputsCSV, JSON (Deeply Nested), API Payload, SQL Insert/Update

Logic EngineVisual Python Construction (No-Code), Template-based Transformation Configs

Primary Use Cases

[01]Data Onboarding: Standardize and validate client CSVs before they enter your system.
[02]Reference Data Enrichment: Replace Excel VLOOKUPs by joining localized lookup tables (e.g., Country Codes, Customer IDs) directly within the transformation pipeline with support for fuzzy matching.
[03]Autonomous Mapping Agents: Deploy async AI workers to heuristically map, validate, and iteratively build and correct transformations for new client files without human intervention.
[04]Legacy Data Migration: Transform extracts from legacy systems into your platform's specific import templates.
[05]Complex API Payload Construction: Convert flat CSV rows into deep-nested JSON bodies for API Post requests.
[06]Cross-Reference Validation: Validate data integrity by checking against reference lookup tables and external APIs/DBs during the transform, supporting fuzzy matching and deduplication.
[07]Non-Technical Logic Definition: Enable non-technical teams to visually construct complex conditional logic (If/Then, Loops) and functions that generate standard Python, removing engineering bottlenecks.

Primary Audience

→Implementation Specialists who onboard new clients.
→Data Migration Consultants moving data between ERPs/CRMs.
→Technical Operations Teams managing recurring file imports.

When Not To Use DataFlowMapper

Real-Time ETL

Do not use for sub-second, real-time event streaming pipelines. DataFlowMapper is optimized for batch-based file onboarding and payload construction.

Simple Replication

Do not use for simple 1:1 database replication where no transformation logic is required. Use dedicated ELT tools for raw replication.

Unstructured Scraping

Do not use for scraping unstructured web data. The engine requires structured or semi-structured inputs (CSV, Excel, JSON).

Architectural Comparison

System Attribute	Excel / Spreadsheets	Scripts / SQL	ETL / iPaaS	DataFlowMapper
Primary Goal	Ad-hoc Analysis	Imperative Logic	Pipeline Orchestration	Payload Construction
Logic Accessibility	Formulas (Fragile)	Code (High Barrier)	Proprietary Nodes	Visual Python (No-Code)
Processing	Manual / Visual	Batch / Memory Heavy	Batch / Stream	Row-Streaming (Stateless)
Input Handling	Heuristic Typing (Inferred)	Library Dependent	Schema Enforcement	Raw String Ingestion
Repeatability	None (Manual)	High (Code Re-use)	Medium (Pipelines)	Versioned Templates
Validation	Visual Inspection	Unit Tests	Schema Enforcement	Row-Level + API Lookup