data-diff is a free, open-source tool that enables data professionals to detect differences in values between any two tables. It's fast, easy to use, and reliable. Even at massive scale.
🗎 Documentation website - our detailed documentation has everything you need to start diffing.
- PostgreSQL >=10
- MySQL
- Snowflake
- BigQuery
- Redshift
- Oracle
- Presto
- Databricks
- Trino
- Clickhouse
- Vertica
- DuckDB >=0.6
- SQLite (coming soon)
For their corresponding connection strings, check out our detailed table.
If a database is not on the list, we'd still love to support it. Please open an issue to discuss it, or vote on existing requests to push them up our todo list.
pip install data-diff
-
pip install 'data-diff[mysql]'
-
pip install 'data-diff[postgresql]'
-
pip install 'data-diff[snowflake]'
-
pip install 'data-diff[presto]'
-
pip install 'data-diff[oracle]'
-
pip install 'data-diff[trino]'
-
pip install 'data-diff[clickhouse]'
-
pip install 'data-diff[vertica]'
-
For BigQuery, see: https://pypi.org/project/google-cloud-bigquery/
Some drivers have dependencies that cannot be installed using pip
and still need to be installed manually.
Once you've installed data-diff
, you can run it from the command line.
data-diff DB1_URI TABLE1_NAME DB2_URI TABLE2_NAME [OPTIONS]
Be sure to read the docs for detailed instructions how to build one of these commands depending on your database setup.
Here's an example command for your copy/pasting, taken from the screenshot above when we diffed data between Snowflake and Postgres.
data-diff \
postgresql://<username>:'<password>'@localhost:5432/<database> \
<table> \
"snowflake://<username>:<password>@<password>/<DATABASE>/<SCHEMA>?warehouse=<WAREHOUSE>&role=<ROLE>" \
<TABLE> \
-k activity_id \
-c activity \
-w "event_timestamp < '2022-10-10'"
Here's a code example from the video, where we compare data between two Snowflake tables within one database.
data-diff \
"snowflake://<username>:<password>@<password>/<DATABASE>/<SCHEMA_1>?warehouse=<WAREHOUSE>&role=<ROLE>" <TABLE_1> \
<SCHEMA_2>.<TABLE_2> \
-k org_id \
-c created_at -c is_internal \
-w "org_id != 1 and org_id < 2000" \
-m test_results_%t \
--materialize-all-rows \
--table-write-limit 10000
In both code examples, I've used <>
carrots to represent values that should be replaced with your values in the database connection strings. For the flags (-k
, -c
, etc.), I opted for "real" values (org_id
, is_internal
) to give you a more realistic view of what your command will look like.
We know that in some cases, the data-diff command can become long and dense. And maybe you're new to the command line.
- We're here to help on slack if you have ANY questions as you use
data-diff
in your workflow. - You can also post a question in GitHub Discussions.
To get a Slack invite - click here
- How to use from the shell (or: command-line)
- How to use from Python
- How to use with TOML configuration file
- Usage Analytics & Data Privacy
- Feel free to open an issue or contribute to the project by working on an existing issue.
- Please read the contributing guidelines to get started.
Big thanks to everyone who contributed so far:
Check out this technical explanation of how data-diff works.
This project is licensed under the terms of the MIT License.