Skip to main content

DashSynthetic — Databricks Library

CI PyPI License

Part of the Dashlibs suite — Databricks libraries built for business users.

Installation

%pip install dash-synthetic

Quick Start

import dashsynthetic
dashsynthetic.launch()   # Opens interactive UI in your Databricks notebook

The UI has two tabs:

  • Single Table — profile a source table/DataFrame/SQL query and generate synthetic data from it.
  • Multi-Table Relationships — define multiple tables, their primary keys, foreign keys, and master data columns (e.g. currency/country codes); the tool figures out the dependency order and generates every table with referentially valid foreign keys.

What it looks like

Single Table — profile a source and generate synthetic data from it:

DashSynthetic single-table tab

Multi-Table Relationships — define tables, primary/foreign keys, and master data columns:

DashSynthetic multi-table relationships tab

Python API

from dashsynthetic import RelationshipGraph, MultiTableGenerator

graph = RelationshipGraph()
graph.add_table("Customer", table="catalog.schema.dim_customer", primary_key="customer_id")
graph.add_table("Account", table="catalog.schema.fact_account", primary_key="account_id",
                master_data_columns=["currency_code"])
graph.add_foreign_key("Account", "customer_id", "Customer", "customer_id")

gen = MultiTableGenerator(graph)
gen.configure_table("Customer", n_rows=5000)
gen.configure_table("Account", n_rows=20000, output_table="catalog.schema.syn_account")
results = gen.run()   # {"Customer": df, "Account": df}, generated in dependency order

Part of Dashlibs

Library Purpose
dash-dq Data Quality
dash-synthetic Synthetic Data Generation
dash-observe Data Observability (freshness, volume, schema)
dash-ml ML Model Monitoring
dash-ingest Data Ingestion
dash-gov Data Governance
dash-ontology Ontology & Lineage for AI
dash-ui Shared UI components (PyPI: dash-uis)

Quality & Contributing

  • 16 unit tests, zero Spark dependency to run them — pytest tests/ -v (the relationship graph, generation ordering, and multi-table orchestration logic are all pure Python and fully covered)
  • Lint-clean (ruff check dashsynthetic/), PEP 561 typed (py.typed)
  • Every change ships through a reviewed pull request; CI (lint → test on Python 3.9–3.12 → build) gates every PR and every release
  • See CONTRIBUTING.md for dev setup, CHANGELOG.md for release history, SECURITY.md to report a vulnerability, and CODE_OF_CONDUCT.md

License

Apache 2.0

Metadata

Release files for dash-synthetic 0.1.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for dash-synthetic 0.1.8
File Size Uploaded
dash_synthetic-0.1.8.tar.gz 288.0 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for dash-synthetic 0.1.8
File Interpreter ABI Platform
dash_synthetic-0.1.8-py3-none-any.whl Python 3 none any Details

Total release size: 318.1 kB

Release files / dash_synthetic-0.1.8.tar.gz

Download URL dash_synthetic-0.1.8.tar.gz
Size 288.0 kB
Tags Source
SHA-256 checksum
How to use checksums
fd1f50d959de7be9ed2763cf198b1770638983b57bca9b2eca4b7f6771867e35
BLAKE2b-256 checksum
How to use checksums
0a6d1bb231c23d85358495372965f981962ec430be6668849729e8f78638fd0f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 24, 2026.

Transparency log

Release files / dash_synthetic-0.1.8-py3-none-any.whl

Download URL dash_synthetic-0.1.8-py3-none-any.whl
Size 30.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
ca04790220c29a2c9c5b0ee7110791bbbec03446a12513edfcfaed2d7d60694c
BLAKE2b-256 checksum
How to use checksums
f49e02a5427055a96e4af3c0d11930771bfdcc914f5cb2d18f19c31126df7f01
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 24, 2026.

Transparency log

Release history Release notifications | RSS feed

0.1.15

2 release files

0.1.13

2 release files

0.1.12

2 release files

0.1.11

2 release files

0.1.9

2 release files

This release

0.1.8 This release

2 release files

0.1.7

2 release files

0.1.6

2 release files

0.1.5

2 release files

0.1.4

2 release files

0.1.3

2 release files

0.1.2

2 release files

0.1.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page