Data Maestro

Data Maestro is a local Mac app for generating synthetic data, anonymizing personally identifiable information, seeding databases, and running a mock REST API. Its generator offers more than 75 field types across five locales, with cross-field consistency, deterministic seeded output, and streaming or parallel processing for millions of rows. A scanner detects emails, phone numbers, SSNs, credit cards, and IP addresses; masking can preserve formats and relationships across live database tables. Listed connectors span relational, document, key-value, graph, time-series, and search databases, as well as HTTP webhooks. It imports files, schemas, and API specifications, and exports more than 30 formats, including SQL dialects, Parquet, Avro, BSON, and OpenAI fine-tuning JSONL. The mock server supports JSON, XML, or CSV responses, pagination, custom headers, CORS, and OpenAPI import. The maker says data processing runs locally, saved connections, templates, and datasets are encrypted at rest with AES-256-GCM, and generated or anonymized data are not uploaded. License activation and update checks make network calls. The native app requires macOS 14 or later on Apple Silicon; a signed CLI is available, while the Docker image is not published to a registry. After the 30-day full trial, the free tier limits exports or database pushes to 100 rows and saved connections to two. Pro is listed at $149 a year.

Data Maestro combines synthetic data generation, PII masking, database seeding, and mock API serving in a local Mac app, with a signed CLI also available. The free tier becomes limited after its 30-day full trial; Pro costs $149 a year.

It is aimed at QA and test engineers, backend and platform teams, frontend and mobile teams, data and compliance teams, solo developers and agencies, and ML and data science teams. It may suit teams that need local data generation, masking, database seeding, or mock APIs.

Data Maestro brings test-data generation, database seeding, PII masking, and mock API serving into a single app. It suits developers and teams working across databases who want data processing to stay local. Its breadth is compelling, but the native app requires a recent Apple Silicon Mac, and the free tier is sharply capped after its trial.

Overview

Data Maestro covers several stages of test-data work in one workflow: create repeatable datasets, mask personal information in live tables, push data into databases, and serve mock REST responses. That breadth may help QA and backend teams reduce tool switching. It also means the product serves a wider purpose than a standalone data generator, though the platform requirement narrows who can use its native app.

The maker says generation, anonymization, and seeding happen locally, with saved connections, templates, and datasets encrypted at rest using AES-256-GCM. The app still contacts the network for license activation and update checks; the maker says generated and anonymized data are not uploaded. That is a useful fit for teams with data-handling concerns, but it is not an offline-only setup.

Key features

Generate and transfer data

More than 75 field types, five locales, cross-field consistency, and deterministic seeds support repeatable datasets with related values. Streaming and parallel generation target runs of millions of rows, which is useful for load and integration work. The Free plan's 100-row cap per export or database push after the trial makes those larger runs a paid-plan use case.

Imports include CSV, TSV, JSON, Excel, database schemas, OpenAPI, and JSON Schema. More than 30 export formats include SQL dialects, Parquet, Avro, BSON, and OpenAI fine-tuning JSONL. That range is a practical strength for teams moving between application databases, analytics formats, and model-training workflows.

Mask live data and serve mocks

The PII scanner looks for emails, phone numbers, SSNs, credit cards, and IP addresses. Format-preserving masking can retain the shape of values, while relationally aware masking applies across live database tables. This makes Data Maestro relevant to teams that need realistic test environments without carrying exposed personal values into them.

Connectors span PostgreSQL, MySQL, SQLite, MSSQL, MongoDB, CouchDB, Firebase, Redis, Neo4j, Cassandra/Scylla, InfluxDB, Elasticsearch, ClickHouse, and HTTP webhooks. Its local REST server can return JSON, XML, or CSV and supports pagination, custom headers, CORS, and OpenAPI-spec import. The combination is broad, although users who only need basic fake rows may find the database and mock-server scope unnecessary.

CLI and deployment

A signed headless CLI supports CI pipelines. The maker describes Linux CLI use, while the Docker image can be built locally but is not published to a registry. That gives pipeline users a route beyond the desktop app, but teams expecting a ready-made registry image will need to build it themselves.

Pricing

The Free plan costs nothing. It includes a 30-day full trial, after which the free tier continues with a 100-row limit per export or database push, up to two saved database connections, and no Mock API server. It is suitable for small experiments and evaluation; the row cap and loss of the mock server make it restrictive for ongoing team workflows.

Pro costs $149 a year, billed annually at approximately $12.42/mo, plus applicable tax. It removes row and database-push limits, allows unlimited saved connections, restores the full Mock API server, and adds live-database PII anonymization, compliance reports, and priority email support. This is the clearest fit for an individual or small team using the full workflow regularly.

Lifetime costs $349, paid once, a one-time payment plus applicable tax. It includes everything in Pro, with no renewals and priority email support. It suits buyers who prefer a one-time purchase to an annual renewal, provided they want the Pro feature set.

Team costs $14,915 a year, billed per seat, plus applicable tax. Each seat gets Pro, with centralized licensing and priority team support; volume pricing is available for 10+ seats. This is aimed at organizations managing multiple users, though the per-seat term means the total grows with team size.

Pro and Lifetime purchases have a 14-day refund policy. Pro and Lifetime include priority email support, while Team includes priority team support.

Platforms

The native SwiftUI app requires macOS 14 (Sonoma) or later on Apple Silicon. Linux is described for CLI use in pipelines, and the product is self-hosted. Buyers needing a native Windows app are not served by the stated platform options.

Who it's for

Data Maestro is a strong fit for QA and test engineers, backend and platform teams, and frontend or mobile developers who need repeatable data across services. Data and compliance teams may value the live-table masking and compliance reports, while ML and data science teams can use the broad export formats. Solo developers and agencies can start with the trial, but the post-trial row limit may push regular use toward Pro.

Pros and cons

• Pro: Generation, relational masking, database seeding, and mock API serving sit in one workflow, covering more of test-data preparation than generation alone.
• Pro: Deterministic generation and cross-field consistency support repeatable test runs, while streaming and parallel modes target very large datasets.
• Pro: Local processing, encrypted saved materials, and a stated no-upload policy for generated or anonymized data help address sensitive-data concerns.
• Con: The native app is limited to macOS 14 or later on Apple Silicon, restricting desktop use for Windows and Intel Mac users.
• Con: After the 30-day trial, Free is limited to 100 rows per export or push and two saved connections, and loses the Mock API server.
• Con: The Docker image must be built locally because it is not available from a registry.

Alternatives

• Windocks is worth considering for teams that need Windows and Linux support or container-based database images; its free plan limits image size to 1 GB and simultaneous containers to one.
• FakerForge offers a free tier with AI tokens, database and table caps, API requests, and one mock API, making it an alternative for users who prefer a free plan organized around those quotas.
• Metasyn is a free, MIT-licensed Python package for users who want an open-source option across Linux, macOS, Windows, and self-hosted environments.
• Mockaroo may suit browser-based generation: its free plan allows 1,000 rows per file and 200 API requests per day, with downloads hosted in the browser.
• Synthetic Data Vault has a free community tier with limited data types, constraints, models, and CSV or Excel support for users comfortable starting with a narrower toolkit.
• Tonic Fabricate offers a free plan with $5 monthly credits and basic exports for users who prefer a credit-based entry point.
• Databucket is a free open-source alternative.
• Faker.js is a free MIT-licensed option for commercial and non-commercial use.

Browse more options in Test Data Management Software.

Verdict

Choose Data Maestro if your team needs one local workflow for generating, masking, seeding, and mocking data across multiple databases and formats. Its strongest reason to buy is that breadth, backed by repeatable generation and a headless CLI. Look elsewhere if you need a native app for Windows or Intel Macs, or if a 100-row free-tier cap cannot support your regular work.