Free online SAV to SQLITE converter

Source format: SAV

SAV is a common file format used by IBM SPSS Statistics and related statistical tools to store dataset information. It typically contains rows of cases and columns of variables, along with rich metadata such as variable labels, value labels, missing-value definitions, measurement levels, and sometimes date/time formatting. Researchers, analysts, social scientists, market researchers, and data teams use SAV files for survey data, experiments, and other structured statistical datasets.

The main advantage of SAV is that it preserves statistical metadata very well, which makes it ideal for analysis in SPSS and compatible tools. However, it is not a general-purpose database format, so it is less convenient for application development, querying, or integration with software that expects a relational database.

Target format: SQLite

SQLite is a lightweight, self-contained relational database format stored in a single file, usually with the .sqlite, .sqlite3, or .db extension. It is widely used by developers, desktop applications, mobile apps, embedded systems, and data workflows that need a portable database without a separate server.

SQLite’s advantages include small size, cross-platform compatibility, and easy access through SQL queries. It is especially useful when you want to move tabular data from a statistical file into a database that can be queried, indexed, and reused by applications.

How to convert SAV to SQLite

The most reliable approach is to export the SAV data to a tabular format and then import it into SQLite, or use a tool that can read SPSS files directly and write SQLite databases. If you want a free local workflow, Python is often the best option because it can read SAV files and create SQLite databases without needing SPSS installed.

Option 1: Use Python with pandas

1. Install the required packages: pip install pandas pyreadstat
2. Read the SAV file in Python using pandas.read_spss() or pyreadstat.read_sav().
3. Create a SQLite database and write the table with DataFrame.to_sql().

Example script:

import pandas as pd
import sqlite3

df = pd.read_spss("input.sav")
conn = sqlite3.connect("output.sqlite")
df.to_sql("data", conn, if_exists="replace", index=False)
conn.close()

This creates a SQLite database file named output.sqlite containing a table called data.

Option 2: Use R

1. Read the SAV file with haven::read_sav().
2. Write the data to SQLite using DBI and RSQLite.

Example:

library(haven)
library(DBI)
library(RSQLite)

df <- read_sav("input.sav")
con <- dbConnect(SQLite(), "output.sqlite")
dbWriteTable(con, "data", df, overwrite = TRUE)
dbDisconnect(con)

Option 3: Use desktop data tools

Some statistical and database tools can open SAV files and export or import data into SQLite-compatible workflows. In many cases, the process is still two-step: open the SAV file, export to CSV or another tabular format, then import that file into SQLite using a database tool such as DB Browser for SQLite.

If you only need a quick conversion and do not want to install software, you can convert the file on 101convert.com for free, directly in your browser.

Quality, compatibility, and limitations

The biggest limitation in this conversion is that SQLite stores tables, not SPSS metadata. While the data values themselves can be transferred, many SAV-specific features may be lost, including variable labels, value labels, missing-value definitions, and measurement levels. If preserving metadata matters, you may need to store it separately in additional SQLite tables or export it as documentation.

Data type mapping is another important consideration. SPSS variables may include numeric, string, date, and special missing-value types, while SQLite uses a simpler type system. During conversion, some values may be converted to text or numeric types depending on the tool used. Very large datasets may also require attention to memory usage if the conversion tool loads the entire file at once.

For best results, use a tool that supports direct SAV reading and SQLite writing, and verify the output by opening the database in a SQLite viewer and checking row counts, column names, and sample values.