AURYNT Data Factory

AURYNT Data Factory treats synthetic data as a product, not a script. A tenant describes the shape of the data they need — schemas, relationships, business rules, volume, behavioural characteristics — and the platform produces a dataset that has been generated, validated against that contract, certified with a quality report, and delivered through the channel the consumer wants.

Teams that need realistic data for development, testing, demos and model work usually get one of three bad options: a masked production copy that still carries risk, a naive random generator that produces data no system behaves realistically against, or a one-off script that nobody can reproduce six months later. None of these can be trusted, versioned, or handed to another team.

• Multi-tenant by design — isolation of schemas, runs, storage and credentials.
• Behavioural realism, not just type-correct rows: distributions, correlations, temporal patterns.
• Every dataset carries a machine-readable certification of what was checked.
• The same definition can produce a batch load, an incremental delta, or a stream.
• Reproducible: a run is defined by a versioned spec plus a seed.

A run begins from a versioned dataset spec. The planner turns the spec into a dependency-ordered set of partitions. Workers generate partitions in parallel and write them to a staging prefix in object storage. Validation reads the staged output, runs the checks declared in the spec, and writes results back to the run registry. Only a run that satisfies its certification thresholds is promoted from staging to the tenant's published prefix, which is what any consumer or delivery channel reads.

Runs are partitioned and each partition is independently retryable; the registry makes retries idempotent so a failed worker re-does only its own partition. A failed validation blocks promotion rather than corrupting a published dataset — the failure mode is 'no new data', never 'wrong data'.

Scale comes from partitioning, not from bigger machines. Generation width is bounded by the dependency graph rather than by table count, so adding workers increases throughput for the independent portion of the schema linearly. Storage is append-only per run, which keeps published datasets immutable and cheap to serve.

Quality is a first-class stage, not an afterthought: schema conformance, referential integrity, declared distribution tolerances, and custom business assertions. Results are persisted per run so quality is comparable across versions of the same dataset.

The platform turns 'we need realistic data' from a recurring one-off engineering task into a repeatable, certifiable delivery. AURYNT is in active development; the figures on this page are design targets and architectural intent, not measured production results.