Best SQL Data Compare Tool in 2026

Reproduce it: two tables, three known differences

A comparison tool is only worth trusting if you have watched it find differences you planted yourself. The script below plants exactly three. It creates two tables and inserts rows — run it against a dedicated sample database, never a production one. A drop script is at the end.

1. Seed the two tables

-- Runs as-is on SQL Server, PostgreSQL, MySQL and SQLite.
CREATE TABLE dc_source (
  product_id INT           NOT NULL PRIMARY KEY,
  name       VARCHAR(60)   NOT NULL,
  price      DECIMAL(10,2) NOT NULL,
  in_stock   INT           NOT NULL
);

CREATE TABLE dc_target (
  product_id INT           NOT NULL PRIMARY KEY,
  name       VARCHAR(60)   NOT NULL,
  price      DECIMAL(10,2) NOT NULL,
  in_stock   INT           NOT NULL
);

INSERT INTO dc_source (product_id, name, price, in_stock) VALUES
  (1, 'Keyboard',  49.00, 12),
  (2, 'Mouse',     19.50, 40),
  (3, 'Monitor',  219.00,  3),
  (4, 'Headset',   79.90,  7);

INSERT INTO dc_target (product_id, name, price, in_stock) VALUES
  (1, 'Keyboard',  49.00, 12),
  (2, 'Mouse',     22.50, 40),
  (3, 'Monitor',  219.00,  3),
  (5, 'Webcam',    59.00, 15);

The three planted differences: product 4 exists only in the source, product 2 has a different price (19.50 against 22.50), and product 5 exists only in the target. Everything else matches, which is the part that matters — a tool that reports four differences here is wrong.

On Oracle, use VARCHAR2(60) and NUMBER(10) / NUMBER(10,2), and run the four INSERT statements one at a time — Oracle has no multi-row VALUES clause.

2. Run the comparison

1. Open More → Data Compare from the main toolbar (the native Connection menu has the same item).
2. Source: your connection, the sample database, table dc_source. Target: the same connection and database, table dc_target. Two tables in one database is a valid pair — only pointing both sides at the identical table is refused.
3. Leave the key mapping alone. Both tables have a primary key on product_id, and that is what Jam SQL Studio matches rows on by default.
4. Click Compare.

Expect three rows back, with colour-coded statuses: added (only in source — product 4), modified (product 2, with the changed price cell highlighted), and deleted (only in target — product 5). Everything identical is filtered out of your way. If a comparison genuinely finds nothing, you get an explicit “Source and target data has no differences” message rather than an empty grid you have to interpret.

3. Read the output before you run it

Tick or untick individual rows, then Generate Script. For this seed you get exactly three statements — one INSERT for product 4, one UPDATE for product 2, one DELETE for product 5 — in a preview you read first. From there, Apply executes it or Save writes it to a file you can put in review or a pipeline.

Every generated script is wrapped in a transaction and states in its own header what that wrap actually guarantees on that engine, on the line starting -- Atomicity:. That is not decoration: on SQL Server a mid-script failure rolls the whole thing back; on Oracle, statements applied from the app are committed as they succeed; on MySQL and SQLite the outcome depends on whether the client running the file stops at the first error. A script that quietly implied “all or nothing” everywhere would be lying on three engines out of five.

4. Clean up

DROP TABLE dc_source;
DROP TABLE dc_target;

Why Teams Choose Jam SQL Studio for Data Compare

Compare table data across databases with sync script generation — included in a full SQL IDE at a fraction of standalone tool prices.