dash-synthetic 0.1.2

Synthetic data generation for Databricks — with a built-in notebook UI

DashSynthetic — Databricks Library

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:

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

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-relate	Ontology & Lineage for AI
dash-ui	Shared UI components (PyPI: dash-uis)

License

Apache 2.0