Engineering & QA

Test like production — without production copies.

Sanitize and hand-built fixtures hide the edge cases that break releases. Generate schema-aware rows that keep joins honest, then drop them into staging or CI.

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Product

Profile the shape, then fill the suite.

Watch the agentic dashboard read the schema and synthesize rows. This is how lower environments get fresh data without a prod dump.

Profile this dataset and generate 10k synthetic rows.

Profiling 24 columns… detected 3 correlations. Generating with schema-aware model.

dataxid_generate · running

Correlation matrix24×24

0.94Fidelity

24/24Columns

10kRows

Why not

Faker passes green. Production still fails.

Independent mock tables break joins. Over-sanitized staging hides the distributions that trigger real bugs. A shared spreadsheet is not a CI dataset.

Rule-based fixtures miss rare states and cross-table relationships
Prod dumps are slow, blocked, or both — so staging drifts
Tests need on-demand rows you can regenerate, not a static extract

Outcome

Refresh staging without a prod dump.

Lower environments lag because copying prod is slow or forbidden. Profile the schema you must honor, generate fresh rows, and keep staging honest without the dump.

Schema-aware generation matches the contract your app already expects
On-demand datasets instead of a spreadsheet edited last quarter

HTML profiling report overview with dataset summary cards and quality alerts. — Profile the schema before you synthesize staging data

Outcome

Keep the joins that mocks break.

Independent fake tables pass green and fail in traffic. Generate multi-table graphs with valid foreign keys so tests exercise real relationships.

Primary and foreign keys stay consistent across the table graph
Learned distributions — not Faker noise that hides edge cases

tables.py

from dataxid import Table

accounts = Table(
    pd.read_csv("accounts.csv"),
    primary_key="account_id",
)
transactions = Table(
    pd.read_csv("transactions.csv"),
    foreign_keys={"account_id": accounts},
)

synthesize.py

synthetic = dataxid.synthesize_tables({
    "accounts": accounts,
    "transactions": transactions,
})

# auto-assigned PKs
synthetic["accounts"]
# sequential rows, valid FKs
synthetic["transactions"]

Outcome

Pin known keys, fill the rest.

CI fixtures need fixed IDs and realistic surrounding rows. Condition on the columns you already know; impute the gaps without rewriting the whole table by hand.

Hold known values fixed while the model completes the row
Fill nulls for incomplete fixtures without inventing a second dataset

Condition on known values

Fix the columns you already know and let the model complete the rest.

conditional.py

conditions = pd.DataFrame({
    "income": [">50K"] * 1000,
})

synthetic = model.generate(
    conditions=conditions,
)

Impute missing cells

Fill nulls with model predictions while every non-null cell stays put.

impute.py

model = dataxid.Model.create(data=df)

filled = model.impute(
    df,
    trials=3,
    pick="mode",
)

Also via

Same engine in CI.

Call the SDK from a pipeline job when you need a fresh dataset on every run — not only from the dashboard.

$pip install dataxid

quickstart.py

import dataxid
import pandas as pd

dataxid.api_key = "dx_..."

df = pd.read_csv("data.csv")
synthetic = dataxid.synthesize(data=df, n_samples=1000)

Refresh a lower environment today.

Generate your first test dataset on the free tier.

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