Anyone who has ever wrangled data from multiple sources knows the pain: load a spreadsheet, load a database table, then spend the next twenty minutes dragging fields into alignment — one by one. Auto Map eliminates that entirely. UNIVCONV now detects your input structure on the fly and maps every recognized field to its output counterpart with a single click.
The Problem Auto Map Solves
Data conversion workflows stall at the mapping step. When source and destination schemas share obvious field names — CustomerID, FirstName, OrderDate — there is no good reason a human should have to drag each one into place. Yet historically that's exactly what every conversion tool has required.
The cost compounds fast. A 60-field table means 60 clicks minimum, even before accounting for mismatches, rechecks, and the inevitable typo in a manually renamed column. Multiply that across the dozens of conversion jobs a busy team runs each week and you're losing hours of focused developer and analyst time to pure mechanical overhead.
Auto Map is the answer to that overhead. When UNIVCONV opens a supported input source, it reads the schema, matches recognized field names to the configured output, and presents a complete, ready-to-run mapping — before you've touched a single control.
See Auto Map in Action
Download UNIVCONV and open any .xlsx, .csv, .dbf, or SQL source — Auto Map handles the field mapping before you touch a single control.
Download UNIVCONVSupported Input Formats
Auto Map activates automatically whenever UNIVCONV detects field metadata in the input source. Every format in the list below is fully supported at launch:
Microsoft Excel (.xlsx)
Header rows are parsed and normalized. Multi-sheet workbooks are handled with per-sheet field detection.
Comma-Separated Values (.csv)
First-row headers are automatically detected. Unicode, UTF-8 BOM, and Windows/Unix line endings are all handled gracefully.
dBASE / FoxPro (.dbf)
Full field descriptor block parsing including field name, type, length, and decimal precision — no manual schema entry required.
SQL Tables (All Engines)
Reads live column metadata from MySQL, PostgreSQL, SQL Server, Oracle, Azure SQL, and more via ODBC or native connectors.
How Auto Map Works
The workflow is the same whether you're targeting SQL Server or any other output format — Auto Map slots right into the existing UNIVCONV conversion flow after the Field Mapper opens. The only difference is what happens after you apply the map.
Open File
Open your source file as normal — an .xlsx workbook, a .csv export, a .dbf table, or a live SQL connection. UNIVCONV reads the schema automatically the moment the file loads.
Field Mapper
The Field Mapper opens showing your source fields on the left and the destination schema on the right. At this point the mapping is blank — ready for you to build manually, or to let Auto Map do it for you.
Auto Map
Click Auto Map. UNIVCONV instantly matches every recognized source field to its destination counterpart — exact name matches are confirmed automatically, alias matches (e.g., ZIP → PostalCode) are flagged for a quick review, and unmatched fields are highlighted so nothing is silently dropped.
Apply & Close
Review the generated map, make any adjustments, then click Apply & Close to lock in the mapping and return to the main conversion window.
From Here, the Path Splits by Output Type
Select SQL Server as your destination. UNIVCONV uses the confirmed field map to generate the correct column bindings for the target table.
Optionally create the destination table on-the-fly using the mapped schema — no need to pre-create the table in SQL Server Management Studio.
Run the conversion. Records stream from the source directly into the mapped SQL table.
Choose your destination format — .xlsx, .csv, .dbf, or any other supported output type. The Auto Map field bindings carry over automatically.
Run the conversion. Output is written using the mapped field order and names — no manual column alignment needed.
The entire pipeline runs locally, in-process — no round trips to a cloud service, no data leaving your environment.
Ready to Map Your Data Automatically?
Download UNIVCONV v4, open your first file, and click Auto Map. The mapping writes itself — you just hit Convert.
Get UNIVCONV at univconv.comSee It in Action
Here's the complete Auto Map workflow from a live session — a .dbf file converted to a SQL Server table in under a minute.
Sample Auto Map Output
Here's what an Auto Map result looks like when converting an .xlsx customer export to a SQL destination table:
| Source Field (Excel) | Destination Field (SQL) | Type | Match |
|---|---|---|---|
CustomerID |
customer_id |
Integer | Exact |
FirstName |
first_name |
VARCHAR(80) | Exact |
LastName |
last_name |
VARCHAR(80) | Exact |
DOB |
birth_date |
DATE | Alias |
ZIP |
postal_code |
CHAR(10) | Alias |
LegacyAcctNum |
— | — | Review |
Fields marked Exact are confirmed automatically. Alias matches are pre-mapped but editable with one click. Review fields are surfaced prominently so you decide their fate — map, ignore, or transform.
Scripting Auto Map via the UNIVCONV API
Power users and CI/CD pipelines can invoke Auto Map programmatically. When autoMap is set to true, the engine generates the mapping and returns any unresolved fields for your inspection before running the conversion:
Available Now — All Plans
Auto Map ships in UNIVCONV Version 4, released today. The feature is enabled by default for all users on every plan tier. Open any supported input source and look for the "Auto Map" button in the Mapping Editor toolbar. No configuration required — just click and go.
What's Coming Next
Auto Map is built around one goal: eliminating the mechanical work between you and a finished conversion. We're extending format support in two waves.
Coming soon: Auto Map will support JSON files and AVRO — the serialization format at the heart of Apache Kafka and real-time data streaming pipelines. If your data moves through Kafka, AVRO support means UNIVCONV will meet it there.
On the roadmap: Parquet support is planned for a later release. Parquet is the dominant format for analytical data lakes — Amazon S3, Azure Data Lake, Databricks, Snowflake, and Apache Spark all run on it. It's a more involved integration, and we want to get it right.
Try Auto Map Today
Try UNIVCONV v4. Open any .xlsx, .csv, .dbf, or SQL source, and click Auto Map. Your first zero-effort field map is waiting.
Download UNIVCONV Version 4