Free online XLSX to DB converter

An XLSX workbook needs conversion when a program or device accepts a database file, commonly a SQLite .db, rather than a spreadsheet. The conversion changes worksheet data into database tables that can be queried, indexed, and opened without Excel.

What the XLSX format is

XLSX is Microsoft Excel’s Office Open XML spreadsheet format, supported by Excel, LibreOffice Calc, Apple Numbers, and other spreadsheet software. It is a ZIP-based package of XML files containing worksheets, cell values, formulas, formatting, charts, and workbook metadata. Businesses and individuals use XLSX for lists, reports, budgets, exports, and other tabular data that people need to edit manually.

What the DB format is

.db is a generic filename extension, not a single database specification. For XLSX conversion, it most often means a SQLite database: a self-contained file containing tables, indexes, views, and SQL metadata. SQLite requires no database server, is easy to copy or archive, and lets applications query structured data with SQL.

A .db file can also belong to Microsoft Access, dBase, or proprietary software. Check the receiving application’s documentation before converting. Renaming workbook.xlsx to workbook.db changes only the filename and does not convert the file.

How to convert XLSX to SQLite DB

Use the 101convert.com converter

You can convert the file for free on 101convert.com without installing software. Upload the XLSX file, choose DB as the output format, start the conversion, and download the result. Because .db can represent different database engines, verify that the generated file is SQLite before opening it in a SQLite-compatible program.

Use Python for a controlled import

Python gives you control over worksheet selection, table names, and the resulting schema. Install the required packages:

python -m pip install pandas openpyxl

Save this script as xlsx_to_sqlite.py beside workbook.xlsx:

import re
import sqlite3
import pandas as pd

input_file = "workbook.xlsx"
output_file = "workbook.db"

def safe_name(value, fallback="table"):
    name = re.sub(r"[^A-Za-z0-9_]+", "_", str(value)).strip("_")
    return name or fallback

def unique_names(values):
    result = []
    used = set()
    for value in values:
        base = safe_name(value, "column")
        name = base
        number = 2
        while name.lower() in used:
            name = f"{base}_{number}"
            number += 1
        used.add(name.lower())
        result.append(name)
    return result

sheets = pd.read_excel(input_file, sheet_name=None, engine="openpyxl")
with sqlite3.connect(output_file) as connection:
    used_tables = set()
    for sheet, frame in sheets.items():
        table = safe_name(sheet, "sheet")
        base = table
        number = 2
        while table.lower() in used_tables:
            table = f"{base}_{number}"
            number += 1
        used_tables.add(table.lower())
        frame.columns = unique_names(frame.columns)
        frame.to_sql(table, connection, if_exists="replace", index=False)

print(f"Created {output_file}")

Run it with:

python xlsx_to_sqlite.py

The script creates one SQLite table per worksheet. Inspect the result with a SQLite command-line tool:

sqlite3 workbook.db
.tables
SELECT * FROM Sheet1 LIMIT 10;

To import one worksheet, replace sheet_name=None with its name. Use if_exists="append" only when the existing table has compatible columns and data types; otherwise the import may fail or produce inconsistent data.

Use a desktop database application

A desktop database program that supports XLSX import can create a SQLite database through a workflow such as Import → Spreadsheet. Select the workbook and worksheet, specify whether the first row contains field names, review inferred column types, and save or export using the SQLite database engine. Menu names differ between applications, so confirm the output engine rather than relying only on the .db extension.

Quality and compatibility limits

• Each worksheet normally becomes a separate table. Import tools do not automatically infer relationships, primary keys, or foreign keys between sheets.
• The first nonempty row is often interpreted as the header. Blank, duplicate, or punctuation-heavy headers may be renamed, rejected, or converted into generic column names.
• Spreadsheet formulas are usually imported as their last cached results, not as executable formulas. Recalculate the workbook in Excel or LibreOffice and save it before conversion if current results are required.
• Charts, formatting, merged-cell layout, comments, conditional formatting, and macros do not belong in ordinary SQLite tables and are normally discarded.
• Mixed-type spreadsheet columns can produce text values or nulls when rows combine numbers, dates, and labels. Review the inferred schema and define explicit types when the receiving application requires them.
• Dates may be stored as text or as Excel serial numbers, depending on the importer. Convert them to an agreed representation, such as ISO 8601 text, when date interoperability matters.
• Test the resulting file with the target program. A valid SQLite database can still be unusable if the application expects a different engine, table name, column name, or schema.