Data Engineering

Need 100k Rows of Dummy Data? Stop Searching.

Testing an LLM workflow, a retrieval system, or a database with ten rows tells you almost nothing. Real testing needs volume. It needs messy variety. It needs data that behaves like production without exposing production records.

That is why the Synthetic Data Generator on DataMasker now supports up to 100,000 records generated entirely in your browser. No accounts, no server-side processing, and no waiting on cloud jobs just to get a usable test dataset.

Why the 100,000-row limit matters

Most free dummy data generators stop being useful the moment you move beyond a quick demo. Many cap output at 100 rows or 1,000 rows, which is nowhere near enough for meaningful stress tests, retrieval evaluation, pagination checks, import workflows, or schema validation at scale.

A browser-based tool that can generate 100,000 synthetic records changes that equation. It gives developers and analysts enough data to test realistic behavior while keeping the workflow fast, local, and private.

What developers get from the updated generator

Instant templates

You can start with preset field collections for common use cases or build a custom schema from scratch. That means less time setting up fake datasets and more time actually testing your systems.

Massive field variety

The generator supports a broad set of field types including names, emails, phone numbers, credit card values, IP addresses, locations, and other structured data that commonly appears in application databases and LLM evaluation sets.

Full schema control

Real systems do not run on generic demo tables. You can customize the schema to match your exact database structure, internal objects, or ingestion format so the exported dataset reflects the shape your code actually expects.

CSV and JSON export

Once generated, the dataset is ready for CSV or JSON export. That makes it easy to drop synthetic records into a RAG pipeline, seed a local database, benchmark import speed, or run stress-testing scripts without extra conversion steps.

Practical use cases for 100k synthetic records

• Benchmark RAG ingestion and chunking pipelines with realistic JSON fixtures.
• Stress test database imports, pagination, sorting, and filter performance.
• Validate analytics dashboards and BI queries without using live customer data.
• Seed staging environments with structured records that preserve privacy.
• Test LLM applications against larger context windows and retrieval volumes.

Local-first speed and privacy

The differentiator is not only the row count. It is the fact that the entire workflow happens in the browser. You can generate large synthetic datasets without creating a new account, sending schemas to a remote server, or waiting for a backend process to build your file.

That gives teams a rare combination: speed, privacy, and scale inside one free tool.

Generate 100k rows now

Open the Synthetic Data Generator and create CSV or JSON datasets that are actually large enough to test something meaningful.