Bauta

Put a mask on production: a safe, realistic stand-in for your production data, in whichever database you need it. Bauta masks what it copies, copies only the slice you need with every relationship intact, generates what may not be copied at all, and moves data between databases on a schedule you already run, with nothing to host.

• Masking: consistent across tables and runs, one-to-one for keys (NIST FF1 where policy requires it), applied before anything reaches the target, and every column must be covered.
• Discovery, subsets and synthetic data: propose a masking policy from a live schema, copy a referentially complete slice of production, create the copy's tables in whichever database it goes to, and fill tables that can't be copied with generated rows.
• Audit: report what every job does with data and what a reviewer should question, and seal each run's masking manifest so it can be verified later.
• Six databases: Oracle, SQL Server, PostgreSQL, MySQL, MariaDB and SQLite, as source or target in any combination.
• Streaming: memory stays flat however large the table, and PostgreSQL and SQL Server targets load in bulk.
• Fast: ten million rows of six masked columns in under two minutes on one core with the optional native masker, and under eight without. Either way the masks are the same.
• Incremental loads: extract only what changed since the last successful run.
• A dependency graph: jobs run in order, concurrently where they can, each in its own process with an optional timeout.
• Operable: run history, Prometheus metrics, webhook alerts, run state in a file or a table, and passwords from a command for cloud IAM tokens.
• No infrastructure: a pip install, some YAML, and a command you run from cron.

Install

Python 3.10 or newer. Choose the drivers you need as extras; each is loaded only when a connection uses it.

pip install "bauta[postgresql,oracle]"

It isn't on PyPI yet. Until the first release, install from a clone:

git clone https://github.com/ribeiro11075/bauta.git
cd bauta
pip install -e ".[postgresql,oracle]"

Building psycopg2. pip compiles it, so it needs a compiler and pg_config:

• macOS: xcode-select --install, then brew install libpq and export PATH="$(brew --prefix libpq)/bin:$PATH" (Homebrew doesn't put libpq on the path by itself).
• Debian or Ubuntu: apt install build-essential libpq-dev.

To skip the build, leave postgresql (and all) out and install the prebuilt driver beside the other extras: pip install "bauta[mysql,oracle,mssql]" psycopg2-binary. psycopg2's maintainers recommend the source build for production.

The native masker (optional). bauta-rs, in mask-rs/, masks in Rust: four to five times the throughput, identical masks, nothing to configure. With Rust 1.83 or newer, pip builds it in the same command as the rest:

pip install -e ".[all]" ./mask-rs/py

Without Rust, leave ./mask-rs/py off; everything works, only slower. See the native masker.

Quickstart

mkdir configuration
cp example/starter/configuration/*.yaml configuration/

Edit configuration/database.yaml and configuration/jobs.yaml for your databases, then supply the credentials they reference:

export SOURCE_DB_PASSWORD=...  TARGET_DB_PASSWORD=...  MASKING_KEY=...

bauta validate          # check the configuration, offline
bauta run --dry-run     # check connections and tables, moving nothing
bauta run               # run every job once

To see it work without any of that, using throwaway SQLite databases:

python example/walkthrough/demo.py       # the whole workflow: discover, subset, audit, mask, verify, synthesize
python example/incremental/demo.py       # streaming and incremental loads
python example/masking/demo.py           # masking, discovery and a subset, from Python
python example/native-masking/demo.py    # the same job masked in Python and in Rust, compared

The command

bauta run              run data jobs once, masking any with a `masking` section
bauta validate         check configuration without connecting
bauta jobs             show the job graph and what's due
bauta history          show recent job outcomes recorded with --history

bauta discover         propose a masking policy for tables
bauta subset           generate jobs that copy a referentially complete subset
bauta schema           create target tables from source ones, in the target's dialect
bauta synthesize       fill tables with generated rows, for data that can't be copied
bauta clear            empty the target tables of jobs, children first

bauta audit            report what each job does with data, and what to question
bauta verify-manifest  check a manifest is unaltered, and who signed it

run makes one pass and exits, so it fits under cron or a Kubernetes CronJob. A second run sharing the same run state refuses to start while the first is still going. The useful flags:

Documentation

Layout

License

MIT