Synthetic Data Generation Cheat Sheet

Practical techniques and libraries for generating synthetic tabular, text, and image data for ML training and privacy-safe testing.

3 PagesIntermediateFeb 11, 2026

Tabular Synthesis with SDV

Fit a generative model on real data and sample synthetic rows.

python

from sdv.metadata import SingleTableMetadatafrom sdv.single_table import CTGANSynthesizerimport pandas as pdreal_data = pd.read_csv("customers.csv")metadata = SingleTableMetadata()metadata.detect_from_dataframe(real_data)metadata.update_column("email", sdtype="email")metadata.update_column("signup_date", sdtype="datetime")synthesizer = CTGANSynthesizer(metadata, epochs=300, verbose=True)synthesizer.fit(real_data)synthetic_data = synthesizer.sample(num_rows=10_000)synthesizer.save("customer_synth.pkl")

Fake Records with Faker

Generate realistic fake PII-free records for seeding dev/test databases.

python

from faker import Fakerfake = Faker("en_US")Faker.seed(42)  # reproducible outputrows = [{    "name": fake.name(),    "email": fake.unique.email(),    "address": fake.address(),    "company": fake.company(),    "ssn": fake.ssn(),  # synthetic, not a real SSN    "created_at": fake.date_time_this_decade().isoformat(),} for _ in range(1000)]

LLM-Generated Text Data

Use a structured-output prompt to mint labeled synthetic training examples.

python

from anthropic import Anthropicclient = Anthropic()resp = client.messages.create(    model="claude-sonnet-4-5",    max_tokens=1024,    messages=[{        "role": "user",        "content": (            "Generate 5 synthetic customer support tickets as JSON array "            "with fields {text, category, priority}. Categories: billing, "            "bug, feature_request. Vary tone and length."        ),    }],)print(resp.content[0].text)

Differential Privacy Noise

Add calibrated Laplace noise so aggregate synthetic stats can't leak individual records.

python

import numpy as npdef laplace_mechanism(true_value, sensitivity, epsilon):    scale = sensitivity / epsilon    noise = np.random.laplace(0, scale)    return true_value + noise# Example: releasing a noisy count with epsilon=1.0 privacy budgettrue_count = 4821noisy_count = laplace_mechanism(true_count, sensitivity=1, epsilon=1.0)

Synthetic Data Tooling Landscape

Where to reach for each modality.

SDV (Synthetic Data Vault)- open-source Python suite for tabular/relational/time-series GANs and copulas
Gretel.ai- managed platform with differential-privacy guarantees and PII detection
Mostly AI- enterprise tabular synthesizer with statistical fidelity reports
Faker / mimesis- lightweight fake-data generators for dev seeding, not statistically representative
Unity Perception / NVIDIA Omniverse Replicator- synthetic image/video for computer vision with ground-truth labels
dbldatagen (Databricks)- Spark-native large-scale synthetic dataframe generation
CTGAN / TVAE- deep generative models underlying most tabular synthesizers

Pro Tip

Always run a fidelity check (SDV's `evaluate_quality` or a simple KS-test per column) before trusting synthetic data downstream — a synthesizer that overfits mode collapse will silently degrade your model's calibration.

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