Using Python to Generate Synthetic Data for Snowflake Data Warehouses

Data engineers spend a surprising amount of time solving a problem that has nothing to do with pipelines, transformations, or performance tuning.

They need data that doesn’t exist yet.

This problem appears across organizations of every size. A team may be building a new data warehouse in Snowflake, developing analytics dashboards, or testing machine learning models, but the necessary datasets may not be available or usable.

The reasons vary:

• Production data may contain sensitive customer information
• New systems may not yet have sufficient historical data
• Engineers may need large volumes of test data
• Data scientists may require training datasets
• Development teams may need repeatable datasets for testing

In these situations, synthetic data becomes a critical tool.

Synthetic data is artificially generated data that statistically resembles real-world datasets but does not contain actual production records. For data engineers, synthetic data enables the creation of realistic datasets that can be safely used for testing, development, and experimentation.

Python provides several powerful libraries for generating synthetic data, including SDV, Faker, Mimesis, and ydata-synthetic. When combined with a modern cloud data platform like Snowflake, these tools allow data teams to rapidly create realistic datasets that support a wide range of business initiatives.

Below are some of the most common business scenarios where synthetic data plays an important role.

Business Use Cases for Synthetic Data

Data Warehouse Development

When building a new data warehouse, engineers often need realistic datasets before operational systems are fully integrated.

Synthetic data allows teams to:

• Populate dimension tables and fact tables
• Validate data models
• Test ETL pipelines
• Validate data quality rules

For example, a data engineer designing a dimensional model in Snowflake might generate synthetic data for tables such as:

• DIM_CUSTOMER
• DIM_PRODUCT
• DIM_DATE
• FACT_SALES

This allows the warehouse to be tested before real production data arrives.

Analytics and Dashboard Development

Business intelligence teams frequently begin developing dashboards before complete data pipelines exist.

Synthetic data enables analytics teams to:

• Test dashboard logic
• Validate metrics and calculations
• Simulate realistic business scenarios
• Demonstrate capabilities to stakeholders

Without synthetic data, dashboard development may stall until production data becomes available.

Machine Learning Model Training

Data scientists often require large datasets to train models effectively. However, production datasets may be restricted due to privacy regulations or insufficient scale.

Synthetic data allows data scientists to:

• Generate training datasets
• Balance class distributions
• Simulate rare events
• Experiment with feature engineering

This capability is particularly valuable in industries with strict regulatory environments such as healthcare and finance.

Performance and Scalability Testing

Modern data platforms must often process billions of records.

Synthetic data enables engineers to:

• Stress test Snowflake workloads
• Benchmark query performance
• Simulate production-scale pipelines
• Evaluate storage and compute costs

By generating large volumes of synthetic data, organizations can evaluate platform performance before go-live.

Data Privacy and Compliance

Many organizations cannot expose production data to development environments due to privacy regulations.

Synthetic datasets allow engineers to:

• Create realistic but non-sensitive datasets
• Share data across teams safely
• avoid exposing personally identifiable information

This makes synthetic data an important tool for organizations working under frameworks such as GDPR or HIPAA.

Python Libraries for Generating Synthetic Data

Several Python libraries provide different approaches to synthetic data generation. Each serves a different purpose depending on the complexity and realism required.

SDV: Generating Statistically Realistic Tabular Data

Synthetic Data Vault (SDV)

SDV (Synthetic Data Vault) is one of the most powerful open-source libraries for generating synthetic tabular data.

Unlike simple random data generators, SDV learns the statistical patterns and relationships within a dataset. It then uses this learned model to generate new records that preserve these relationships.

Best Use Cases

SDV is ideal for:

• Synthetic data warehouse tables
• Machine learning training data
• Privacy-preserving data sharing
• Multi-table relational datasets

Because it learns correlations between columns, SDV can produce datasets that closely resemble the behavior of real-world data.

Example Implementation

A data engineer might begin by loading a source dataset and allowing SDV to learn its structure.

from sdv.single_table import GaussianCopulaSynthesizer
from sdv.metadata import SingleTableMetadata
import pandas as pd

data = pd.read_csv("sales_data.csv")

metadata = SingleTableMetadata()
metadata.detect_from_dataframe(data)

synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.fit(data)

synthetic_data = synthesizer.sample(100000)        

The result is a synthetic dataset that maintains statistical properties of the original data.

Loading Synthetic Data into Snowflake

Once generated, the dataset can be loaded into Snowflake using the Python connector.

Snowflake

import snowflake.connector

conn = snowflake.connector.connect(
    user="USER",
    password="PASSWORD",
    account="ACCOUNT"
)

cursor = conn.cursor()

for row in synthetic_data.values:
    cursor.execute(
        "INSERT INTO FACT_SALES VALUES (%s, %s, %s, %s)",
        row
    )        

In production environments, engineers typically stage the data in files and load it using Snowflake’s COPY INTO command for greater efficiency.

Faker: Generating Realistic Identity Data

Faker

Faker is one of the most widely used Python libraries for generating fake identity data.

It can generate:

• Names
• Addresses
• Emails
• Phone numbers
• Companies
• Job titles

Best Use Cases

Faker is particularly useful when populating dimension tables in a data warehouse.

Examples include:

• DIM_CUSTOMER
• DIM_EMPLOYEE
• DIM_SUPPLIER

Example Implementation

from faker import Faker
import pandas as pd

fake = Faker()

data = [{
    "customer_name": fake.name(),
    "email": fake.email(),
    "city": fake.city(),
    "country": fake.country()
} for _ in range(10000)]

df = pd.DataFrame(data)        

The generated dataset can then be written to Snowflake tables using the same loading techniques described earlier.

Faker provides a quick way to generate realistic descriptive attributes that make synthetic datasets appear believable.

Mimesis: High-Performance Synthetic Data Generation

Mimesis

Mimesis is another Python library designed for generating synthetic identity data.

It serves a similar purpose to Faker but is optimized for performance and internationalization.

Best Use Cases

Mimesis is particularly useful when:

• generating very large datasets
• supporting multi-language environments
• building global customer datasets

Example Implementation

from mimesis import Person
from mimesis import Address

person = Person()
address = Address()

record = {
    "name": person.full_name(),
    "city": address.city(),
    "country": address.country()
}        

Because of its performance characteristics, Mimesis is often used when generating millions of records for performance testing.

ydata-synthetic: Deep Learning for Synthetic Data

ydata-synthetic

The ydata-synthetic library uses machine learning models such as Generative Adversarial Networks (GANs) to create synthetic datasets.

GANs learn complex patterns within datasets and generate highly realistic synthetic data.

Best Use Cases

This library is particularly effective for:

• complex datasets with many relationships
• machine learning training data
• advanced analytics experiments

Example Implementation

from ydata_synthetic.synthesizers import RegularSynthesizer

synthesizer = RegularSynthesizer()
synthesizer.fit(data)
synthetic_data = synthesizer.sample(10000)        

Because these models capture deeper statistical relationships, they can generate datasets that closely resemble production environments.

Combining These Libraries in a Data Engineering Workflow

In practice, data engineers often combine these tools to generate more realistic datasets.

A typical workflow might look like this:

1. Use Faker or Mimesis to generate identity attributes such as names and addresses.
2. Use SDV to model statistical relationships within structured data.
3. Use ydata-synthetic to generate large machine-learning-ready datasets.
4. Load the resulting datasets into Snowflake using Python connectors or staged file loads.

The resulting data can populate entire dimensional models.

Example tables might include:

DIM_CUSTOMER
DIM_PRODUCT
DIM_DATE
FACT_SALES
FACT_ORDERS        

These datasets allow engineers to simulate real business activity at scale.

Synthetic Data as a Data Engineering Capability

Synthetic data is quickly becoming a core capability for modern data engineering teams.

As organizations increasingly adopt cloud data platforms like Snowflake, the ability to generate realistic datasets on demand enables faster development cycles, safer experimentation, and more robust testing environments.

Python libraries such as SDV, Faker, Mimesis, and ydata-synthetic provide powerful tools for building these datasets.

By combining these libraries with scalable cloud data platforms, data engineers can create synthetic environments that closely resemble production systems—without exposing sensitive information.

In many cases, this capability can accelerate development timelines from months to days.

And in a world where data drives nearly every business decision, that acceleration can make a meaningful difference.