datadiffer
Read-only table diffs with segment attribution: which slice of your data changed.
Open source Open in the app JSON README (API)
About
Read-only table diffs with segment attribution: which slice of your data changed.
Details
- Kind
- MCP servers
- Topic
- Marketing & analytics
- Publisher
- gauthierpiarrette
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.1.0
- Last push
- 2026-07-28T10:57:37Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 03:02:50
- Updated
- 2026-08-29 03:02:50
- Origin id
io.github.gauthierpiarrette/datadiffer
README
<!-- mcp-name: io.github.gauthierpiarrette/datadiffer -->
# datadiffer
**Diff two tables. See what changed, and which slice of your data it's concentrated in.**
```
98.6% of modified rows have country = 'DE' (8.87x over-represented)
```
[](https://pypi.org/project/datadiffer/)
[](https://github.com/gauthierpiarrette/datadiffer/actions/workflows/ci.yml)
[](LICENSE)
[](https://pypi.org/project/datadiffer/)
`datadiffer` compares two tables (dev vs. prod, PR vs. main, source vs.
destination) and tells you **what changed and where it's concentrated**: rows
added / removed / modified, per-column change rates, and segment attribution.
Built for the era when agents write your pipelines and someone has to check
their work. A maintained, MIT-licensed successor to the archived
[data-diff](https://github.com/datafold/data-diff).
## Try it in 60 seconds
No credentials, no config, no account. It generates its own data:
```bash
uvx datadiffer demo
```
```
datadiffer-demo/orders_prod.parquet vs datadiffer-demo/orders_dev.parquet
key: order_id (inferred from *_id naming)
rows: +800 added -250 removed ~432 modified 49318 unchanged
┏━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃column ┃ changed rows ┃ % of matched┃
┡━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│amount │ 432 │ 0.87%│
└───────┴──────────────┴─────────────┘
-> 98.6% of modified rows have country = 'DE' (8.87x over-represented)
93.2% of added rows have plan = NULL (63.19x over-represented)
sample changes:
[order_id=0] amount: 5.0 -> 7.1
[order_id=1] amount: 6.37 -> 6.38
[order_id=117] amount: 32.4 -> 34.5
[order_id=234] amount: 59.8 -> 61.9
[order_id=351] amount: 87.2 -> 89.3
DIFF FOUND
```
That last block is the point. A diff tells you 432 rows changed; datadiffer
tells you they're almost all German orders, which is the difference between
"something moved" and "the VAT change landed."
## Install
```bash
pip install datadiffer # local files: Parquet, CSV, DuckDB
pip install 'datadiffer[snowflake]' # + Snowflake
pip install 'datadiffer[postgres]' # + Postgres
pip install 'datadiffer[mcp]' # + MCP server for AI agents
```
## Use it
```bash
# local files
datadiffer diff prod.parquet dev.parquet
# DuckDB
datadiffer diff prod.duckdb:orders dev.duckdb:orders
# Postgres (attached read-only)
datadiffer diff orders orders_v2 --source "postgresql://user@host:5432/db"
# Snowflake, or anything in datadiffer.toml (run `datadiffer init` first)
datadiffer diff wh::analytics.orders wh::analytics_dev.orders
# cross-source: warehouse vs. a local file
datadiffer diff wh::orders orders_snapshot.parquet
# machine-readable, for scripts and CI
datadiffer diff a.parquet b.parquet --format json
```
Exit codes follow the GNU diff convention: **0** no differences, **1**
differences found, **2** operational error.
Useful flags: `--key` (inferred when omitted), `--where`, `--columns` /
`--exclude-columns`, `--sample-limit`, `--no-attribution`, `-q`.
## In your pull requests
[datadiffer-action](https://github.com/gauthierpiarrette/datadiffer-action)
posts the report as a sticky PR comment:
> ### datadiffer: `ANALYTICS.ORDERS` vs `ANALYTICS_PR_482.ORDERS`: differences found ❌
>
> **+800 added · −250 removed · ~432 modified · 49,318 unchanged** (2.96% of base rows affected)
> Policy: max-changed-rows-pct exceeded: 2.96% > 0.5%
>
> | Column | Changed rows | % of matched |
> |---|---:|---:|
> | `amount` | 432 | 0.87% |
>
> > **Where it's concentrated:** 98.6% of modified rows have `country = 'DE'` (8.87× over-represented)
> > 93.2% of added rows have `plan = NULL` (63.19× over-represented)
```yaml
- uses: gauthierpiarrette/datadiffer-action@v1
with:
config: datadiffer.toml # holds the warehouse connection
table-a: wh::ANALYTICS.ORDERS
schema-map: "ANALYTICS=ANALYTICS_PR_${PR_NUMBER}"
max-changed-rows-pct: "0.5"
env:
SNOWFLAKE_PRIVATE_KEY: ${{ secrets.SNOWFLAKE_PRIVATE_KEY }}
```
Running dbt? [docs/dbt-ci.md](docs/dbt-ci.md) turns `dbt ls --select
state:modified` into one diff per changed model, each with its own comment.
## For AI agents (MCP)
Let a coding agent verify its own data changes:
```bash
datadiffer init # scaffold datadiffer.toml, then add the server to your client
```
```json
{ "mcpServers": { "datadiffer": { "command": "datadiffer", "args": ["mcp"] } } }
```
Any MCP client works. `datadiffer init` prints the server entry to paste,
including a `uvx` form for clients that prefer no global install.
Four read-only tools: `list_connections`, `schema_diff`, `diff_summary`
(cheap preflight), `diff_tables` (full report). **Credentials never pass
through the model.** Tools take connection *names* from your local
`datadiffer.toml`, never DSNs. Over-cap requests come back as structured
refusals with a remedy, so the agent can self-correct instead of failing.
## What makes it different
Most diff tools answer *whether* two tables match. datadiffer answers *what
changed and where*: which columns moved, and which slice of the data the
change is concentrated in.
| Tool | Use it instead when |
|---|---|
| [Datafold](https://www.datafold.com/) | You need billion-row cross-database reconciliation, a collaboration UI, lineage/impact analysis, and a support contract. |
| [SQLMesh `table_diff`](https://sqlmesh.readthedocs.io/) | You already run SQLMesh and diff within a single gateway (cross-database diffing is a Tobiko Cloud feature). |
| [Recce](https://github.com/DataRecce/recce) | You want a dbt-native PR review UI and your whole workflow is dbt. |
| [Google DVT](https://github.com/GoogleCloudPlatform/professional-services-data-validator) | You're validating a migration across 17 warehouse types and want pass/fail validation with YAML configs. |
| [datacompy](https://github.com/capitalone/datacompy) | You're comparing two pandas/Spark/Polars DataFrames inside one process. |
datadiffer is for the case in between: one command, no platform, works from
your terminal, your CI, or your agent, and it explains itself.
## Migrating from data-diff / reladiff
Same job, different flags:
| data-diff / reladiff | datadiffer |
|---|---|
| `data-diff DB1 table1 DB2 table2` | `datadiffer diff <a> <b>` (locators or `--source`/`--target`) |
| `-k`, `--key-columns` | `--key` (repeatable, or comma-separated) |
| `-c`, `--columns` | `--columns` |
| `-w`, `--where` | `--where` (validated: single boolean expression, no subqueries) |
| `--json` | `--format json` (stable [schema v1](src/datadiffer/report/json_schema.py)) |
| `-l`, `--limit` | `--sample-limit` |
Differences worth knowing: keys are inferred from declared constraints or
`*_id` conventions when you omit `--key`; unknown column names in filters are
an **error**, never silently ignored; and exit code 1 means "diff found"
(2 is reserved for operational errors), so CI can tell them apart.
**Not yet ported:** checksum-bisection hashdiff for very large cross-database
diffs, which is reladiff's headline capability, plus MySQL, Oracle,
ClickHouse, Trino and the rest of its connector list. If you need those today, use
[reladiff](https://github.com/erezsh/reladiff); the bisection port is the top
item on our v0.2 roadmap and will credit its author.
Full command mapping and behavior differences:
**[docs/migrating-from-data-diff.md](docs/migrating-from-data-diff.md)**.
## Scope and limits (v0.1)
- **Warehouses:** Snowflake, Postgres, DuckDB, Parquet, CSV. BigQuery is next (v0.1.1).
- **Every diff runs in local DuckDB.** Postgres is attached read-only and
scanned in place; Snowflake is pulled once over Arrow with only the needed
columns. Both are capped at 50M rows and 10 GiB per side, so narrow the
comparison with `--where`. Pushing the comparison into the warehouse, and lifting that cap
with checksum bisection, are the next two pieces of work.
- Postgres reads are unsnapshotted (the report says so in `execution.snapshot`);
Snowflake pulls are a single consistent SELECT.
- Segment attribution is single-column, categorical, and descriptive. It says
"over-represented", never causal.
## Project
MIT, no CLA, no telemetry. The [report schema](src/datadiffer/report/json_schema.py)
is frozen at v1 and contract-tested, so pipelines and agents that parse it keep
working across releases. Dependencies are deliberately few: DuckDB, PyArrow,
sqlglot, click, rich.
## What's next
BigQuery, then checksum-bisection hashdiff so large cross-database diffs stop
needing a row cap (ported with credit to reladiff), then more connectors.
Tracked in [issues](https://github.com/gauthierpiarrette/datadiffer/issues).
Comment on the one you need and it moves up.
## Contributing
```bash
git clone https://github.com/gauthierpiarrette/datadiffer && cd datadiffer
uv sync --group dev
uv run pytest -q # warehouse tests skip unless credentials are set
uv run ruff check .
```
Prior art gratefully acknowledged: [data-diff](https://github.com/datafold/data-diff)
and [reladiff](https://github.com/erezsh/reladiff) (Erez Shinan), whose
checksum-bisection design v0.2 will port; Adtributor (Microsoft Research) for
the attribution scoring; and DuckDB, which makes all of this fast enough to be
boring.
## License
MIT