sqllocks-spindle 2.2.4

Multi-domain, schema-aware synthetic data generator for Microsoft Fabric

"Synthea is to MITRE as Spindle is to SQLLocks"

Spindle is a multi-domain, schema-aware synthetic data generator for Microsoft Fabric. It generates statistically realistic, relationally correct datasets — think normalized 3NF schemas with proper FK integrity, Pareto order distributions, seasonal temporal patterns, and real US addresses with lat/lng coordinates ready for Power BI maps.

pip install sqllocks-spindle

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Why Spindle?

Every Fabric project starts with the same problem: where's the test data?

Random data generators give you Customer_001 buying Product_ABC for $10.00. Dashboards look flat. Pipelines pass testing but fail on real cardinality. ML models train on data with no signal to find.

Spindle generates data that looks and behaves like production data — without any real data involved:

• 13 pre-built domains with distributions sourced from published data (BLS, NAIC, NCES, NAR, FDIC, Federal Reserve, SEC, and 40+ more)
• Schema-aware generation — tables generated in dependency order, FK integrity guaranteed, composite keys handled
• Chaos engine — intentionally corrupt data (nulls, duplicates, schema drift) to stress-test your pipeline
• Fabric-native — write directly to Lakehouse, Warehouse, SQL Database, Eventhouse, and Semantic Models
• Transparent — every generation rule is a human-readable .spindle.json schema you can inspect and version control

Unlike ML-based generators (SDV, MOSTLY AI, Gretel), Spindle doesn't need training data. Unlike Faker, it produces relationally correct, statistically calibrated datasets at any scale.

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Documentation

I want to...	Go to
Get started in 5 minutes	Quickstart (Python)
Use the CLI without writing Python	Quickstart (CLI)
Generate data in a Fabric notebook	Quickstart (Fabric)
Follow step-by-step tutorials	Tutorials (17 learning paths)
Understand a specific feature	Guides (18 feature guides)
Run working example code	Examples (22 scripts + 35 notebooks)
Browse the API	API Reference
See all 13 domains	Domain Catalog
Check calibration sources	Methodology

Full docs site: sqllocks.github.io/spindle

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Quick Start

from sqllocks_spindle import Spindle, RetailDomain

spindle = Spindle()
result = spindle.generate(
    domain=RetailDomain(),
    scale="small",
    seed=42
)

print(result)
# GenerationResult(9 tables, 21,300 total rows, 0.3s)

# Access any table as a pandas DataFrame
customers = result["customer"]
orders    = result["order"]
addresses = result["address"]

# Check referential integrity
errors = result.verify_integrity()
assert errors == []

# Print a generation summary
print(result.summary())

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Domains

Spindle ships 13 production-ready domains — each with calibrated distribution profiles, referential integrity enforcement, and 1,250+ passing tests:

Domain	Tables	Description
Retail	9	Customers, products, orders, returns — 3NF normalized
Healthcare	9	Patients, encounters, diagnoses, claims — 3NF normalized
Financial	10	Branches, accounts, transactions, loans, fraud detection
Supply Chain	10	Warehouses, suppliers, POs, inventory, shipments
IoT	8	Devices, sensors, readings, alerts, maintenance
HR	9	Employees, departments, compensation, performance
Insurance	9	Agents, policies, claims, underwriting, payments
Marketing	10	Campaigns, contacts, leads, opportunities, conversions
Education	9	Students, courses, enrollments, grades, financial aid
Real Estate	9	Agents, listings, offers, transactions, inspections
Manufacturing	9	Production lines, work orders, quality control, equipment
Telecom	9	Subscribers, service lines, usage records, billing, churn
Capital Markets	10	S&P 500 companies, daily prices (GBM), dividends, earnings, trades

Each domain ships with calibrated distribution profiles based on real-world data (see METHODOLOGY.md).

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Retail Domain

The built-in RetailDomain generates a fully normalized retail schema:

Table	Small scale	Description
customer	1,000	Individual customers with loyalty tiers
address	1,500	Shipping/billing addresses with real US lat/lng
product_category	50	3-level hierarchy (dept → category → subcategory)
product	500	SKUs with correlated cost/price
store	150	Physical and online stores
promotion	200	Discount campaigns
order	5,000	Order headers with Pareto customer distribution
order_line	~12,500	Line items with discount_percent
return	~850	Returns with dates derived from order dates

Scale presets: small, medium (50K customers), large (500K), xlarge (5M)

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Healthcare Domain

The HealthcareDomain models clinical encounters, claims, and medications:

Table	Small scale	Description
provider	200	Physicians, NPs, PAs with credentials
facility	50	Hospitals, clinics, urgent care centers
patient	1,000	Patient demographics and insurance
encounter	5,000	Office visits, ED, inpatient, telehealth
diagnosis	~9,000	ICD-10 codes linked to encounters
procedure	~6,000	CPT procedures with charges
medication	~4,500	Prescriptions with dosage and supply
claim	~4,750	Insurance claims with status
claim_line	~11,875	Claim line items with copays and adjustments

All distributions calibrated from CMS, CDC, AAMC, KFF, and BLS data — see METHODOLOGY.md.

What makes it realistic

• Pareto orders — 20% of customers place 80% of orders (max_per_parent=50 hard cap)
• Seasonal patterns — November/December peaks, Friday/Saturday peaks, bimodal hour distribution
• Real addresses — 40,977 US ZIP codes from GeoNames (CC-BY-4.0): city, state, ZIP, lat, lng. Works directly in Power BI map visuals.
• Correlated cost/price — product cost is always 30–70% of unit price
• Proper hierarchy — product categories form a real 3-level tree
• Business rules enforced — return dates always after order dates, order dates after signup dates

Address data for Power BI

addr = result["address"]
print(addr[["city", "state", "zip_code", "lat", "lng"]].head())
#            city state zip_code        lat         lng
# 0        Reform    AL    35481  33.314928  -88.042923
# 1       Chinook    MT    59523  48.487741 -109.261678

Drop the lat/lng columns directly into a Power BI map visual — no geocoding required.

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Generation Strategies

Spindle supports 21 column-level strategies:

Strategy	Description
sequence	Auto-incrementing integer PKs
uuid	UUID v4 alternative PKs
faker	Faker library providers (names, emails, etc.)
weighted_enum	Weighted random selection from a set of values
distribution	Statistical distributions: uniform, normal, log_normal, pareto, zipf, geometric, bernoulli, bimodal
temporal	Time-aware dates: uniform or seasonal with day/month/hour profiles
formula	Computed from other columns: quantity * unit_price * (1 - discount_percent / 100)
derived	Derived from another column with a transformation: return_date = order_date + N days
correlated	Mathematically related to another column: cost = unit_price * 0.30–0.70
conditional	Conditional on another column's value
lifecycle	Phase-based status values (introduced / active / discontinued)
foreign_key	FK references with uniform, Pareto, or Zipf distribution
lookup	Copy value from parent table via FK
reference_data	Pick from bundled JSON datasets
pattern	Formatted strings: Store #{seq:4}
computed	Aggregated from child table (e.g., order_total = sum of line_totals)
self_referencing	FK to same table for hierarchy columns
self_ref_field	Read level info stashed by self_referencing
record_sample	Sample complete records from a reference dataset (anchor)
record_field	Read a field from a previously sampled record (correlated derived columns)
scd2	SCD Type 2 versioning: effective_date, end_date, is_current, version

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Distribution Profiles

Every domain ships with a default profile calibrated from real-world data. You can override any distribution weight:

# Override specific distributions
domain = RetailDomain(overrides={
    "customer.loyalty_tier": {"Basic": 0.40, "Silver": 0.30, "Gold": 0.20, "Platinum": 0.10},
    "order.status": {"completed": 0.85, "shipped": 0.05, "processing": 0.02, "cancelled": 0.03, "returned": 0.05},
})

# Use a named profile
domain = HealthcareDomain(profile="medicare")

# Check what's available
print(domain.available_profiles)   # ['default']
print(domain.profile_name)         # 'default'

Profile files live in domains/<name>/profiles/ and follow the same JSON schema as default.json. See METHODOLOGY.md for the full list of distribution keys and their real-world sources.

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Custom Schemas

from sqllocks_spindle import Spindle

spindle = Spindle()
result = spindle.generate(
    schema="path/to/my_schema.spindle.json",
    scale_overrides={"customer": 10000, "order": 100000},
    seed=42
)

Schemas are defined in .spindle.json files. See PHASE-0-SPEC.md for the full schema definition format.

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CLI

Spindle provides 21 CLI commands covering generation, export, inference, incremental workflows, and Fabric publishing.

Core Generation

spindle generate retail --scale small --seed 42 --output ./output/
spindle generate healthcare --scale small --format parquet --output ./data
spindle generate retail --scale medium --dry-run

Export & Transform

spindle to-star retail --scale small --output ./star/
spindle to-cdm retail --scale small --output ./cdm/
spindle export-model retail --output retail.bim --source-type lakehouse

Schema Import & Inference

spindle from-ddl my_tables.sql --output my_schema.spindle.json
spindle learn ./real_data/ --format csv --output inferred.spindle.json
spindle compare ./real/ ./synthetic/ --format csv --output report.md
spindle mask ./real_data/ --output ./masked/ --seed 42

Incremental & Day 2

spindle continue retail --input ./day1/ --output ./deltas/ --inserts 100
spindle time-travel retail --months 12 --output ./snapshots/ --growth-rate 0.08

Multi-Domain

spindle composite enterprise --scale small --output ./data/ --format parquet
spindle composite retail+hr+financial --scale medium --output ./enterprise/
spindle presets

Streaming & Simulation

spindle stream retail --table order --max-events 5000 --sink file --output events.jsonl
spindle stream retail --table order --rate 100 --realtime --burst 30:60:10

Fabric Publishing

spindle publish retail --target lakehouse --base-path "abfss://..." --scale small
spindle publish retail --target sql-database --connection-string "env://SPINDLE_SQL_CONNECTION"
spindle publish retail --target eventhouse --connection-string "https://..." --database mydb

Performance (v2.2.0): FabricSqlDatabaseWriter uses fast_executemany with vectorized coercion (~24s for 100K rows). WarehouseBulkWriter uses parallel multi-file COPY INTO staging per MS Learn guidelines — stage all Parquet chunks first, then one wildcard COPY INTO per table with concurrent table loading via ThreadPoolExecutor. Scale tiers up to xxxl (~1T rows).

Discovery & Profiles

spindle list
spindle describe retail
spindle validate my_schema.spindle.json
spindle profile list retail
spindle profile export retail --output retail_profile.json

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Development

# Create virtual environment
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Regenerate bundled address data from GeoNames
python scripts/build_address_data.py

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Third-Party Data

Address data (city, state, ZIP, lat, lng) is sourced from GeoNames under Creative Commons Attribution 4.0 International (CC-BY-4.0). See LICENSE-NOTICES.md.

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License

MIT — see LICENSE

Roadmap

• Phase 0 ✅ Core engine, 21 strategies, Retail + Healthcare domains, calibrated profiles
• Phase 1 ✅ Fabric Lakehouse writer, CSV/Parquet/Delta/JSONL/Excel output, CLI
• Phase 2 ✅ Streaming engine, AnomalyRegistry, Event Hub + Kafka sinks, spindle stream CLI
• Phase 3 ✅ Domain expansion — 13 domains, shared reference data
• Phase 4 ✅ MCP server bridge, PyPI packaging, GitHub Actions CI/CD
• Phase 5 ✅ Star schema output, CDM export, spindle to-star / spindle to-cdm CLI
• Tier 1 ✅ MkDocs site, 17 doc guides, 4 tutorial notebooks, GenerationResult convenience methods
• Tier 2 ✅ SQL/DDL pipeline, FabricSqlDatabaseWriter, Capital Markets domain, star/CDM maps for all 13 domains, 12 notebooks
• Tier 3 ✅ Inference engine (spindle learn/compare/mask), incremental engine (spindle continue), SCD2, time-travel snapshots, composite presets, 11 notebooks
• Blueprint ✅ Credential resolver, spindle publish CLI, Eventhouse writer, observability, 6 simulation pattern modules (clickstream, IoT telemetry, financial streams, operational logs, workflow state machines, SCD2 file drops), acceptance tests, provisioning guide
• Launch ✅ v2.2.0 — boolean fix, COPY INTO perf, xxl/xxxl scale tiers, 22/23 integration sweep PASS