Are the General Enterprise records on this page real?

No. Every record on this page is synthetic, generated from a fixed seed by the pack's own generator; masked values come from the real masking engine. The masking example uses a fixed public sample key; your own data is masked with a key from your secret store.

How long does a first certified General Enterprise dataset take?

Estimates, not guarantees — try the synthetic sandbox: minutes; install (or reuse) the agent: under an hour; connect a source: under an hour; review and approve policies: under an hour; first certified dataset: minutes.

Which test scenarios does the General Enterprise pack include?

9 runnable scenarios (seasonal peak, backorders, cancellations returns, late shipments and more), plus 1 negative-test scenario kept apart from valid data.

Do General Enterprise rows leave my network?

No. The DataNivra agent runs inside your environment: it reads the source, masks, subsets, generates and certifies there, and sends only metadata, aggregate counts and evidence to the DataNivra control plane (customer-resident processing, zero raw-production-data egress).

Industry packs

General Enterprise industry pack

Customer, order and support test data that matches across CRM, ERP and support.

Customers, orders, products, suppliers, employees, contracts, invoices and payments

Fully self-service (journey proven end to end) Version 2.0.0 · Available in paid plans

The full General Enterprise 2.0.0 journey — from the public pack page through the synthetic sandbox, signup, activation, agent and source setup, scenario, masking and subsetting, certification and provisioning to evidence export — was verified end to end on a local real-component stack (the real control plane, agent, engine, sandbox runner, website and console built from source, with synthetic data) on 2026-10-09. Published agents 0.4.4, 0.4.3, 0.4.2, 0.4.1 and 0.4.0 run this pack version.

Try with synthetic data Activate this pack — start free

No call and no credit card needed. Prefer a conversation? Request a demo (optional). Missing something? Request a feature.

Included in Free Trial, Developer, Team, Business, Enterprise and Private Cloud / Self-Hosted. You can activate it for your organization yourself in the console; no call is needed.

Problems this pack solves

Masked data that still joins across 5 systems

6 cross-system relationships link commerce, crm, finance, hr and procurement; the pack's masking keeps one pseudonym per identity on every side. For example, crm.customers.customer_id and commerce.orders.customer_ref get the same pseudonym.

The cases production samples rarely contain

6 ready-made scenarios generate them on demand, for example: out-of-stock products and back-ordered lines awaiting replenishment; cancelled and returned orders with matching line statuses and refunds; shipments or go-lives that missed the promised date by one to eight weeks.

Sensitive fields found and masked before anyone sees them

35 columns across 10 entities are classified (direct identifier, financial, PII, quasi identifier and sensitive) and covered by 3 masking templates you review and approve.

Evidence that each dataset is fit to use

3 certification presets check masking coverage, referential integrity, orphans and row counts before a dataset can be provisioned; a failed dataset is never provisioned.

Entities and relationships

10 entities across 5 source systems, generated from the pack’s own entity model.

Arrows point from the referencing entity to the one it references. Solid: a foreign key inside one system. Dashed: a cross-system relationship — the pack keeps the same pseudonym on both sides, so masked data still joins.

All 11 relationships as a table

Relationships of the General Enterprise pack
Entity	References	Columns	Kind
OrderLine	Order	`order_id` → `order_id`	Within a system
OrderLine	Product	`product_id` → `product_id`	Within a system
Contract	Supplier	`supplier_id` → `supplier_id`	Within a system
Employee	Location	`location_id` → `location_id`	Within a system
Payment	Invoice	`invoice_id` → `invoice_id`	Within a system
Order	Customer	`customer_ref` → `customer_id`	Across systems (pack relationship template)
Order	Employee	`sales_rep_ref` → `employee_id`	Across systems (pack relationship template)
Product	Supplier	`supplier_ref` → `supplier_id`	Across systems (pack relationship template)
Invoice	Order	`order_ref` → `order_id`	Across systems (pack relationship template)
Invoice	Customer	`customer_ref` → `customer_id`	Across systems (pack relationship template)
Contract	Employee	`owner_employee_ref` → `employee_id`	Across systems (pack relationship template)

Realistic synthetic records

Synthetic data. Every record on this page is synthetic, generated from a fixed seed by the pack's own generator; masked values come from the real masking engine.

Entity

Customer — `crm.customers` (synthetic)
`customer_id` sensitive	`first_name` sensitive	`last_name` sensitive	`email` sensitive	`phone` sensitive	`street_address` sensitive	`city` sensitive	`postal_code` sensitive	`company_name`	`segment`	`created_on`	`marketing_opt_in`	`notes` sensitive
CUS00000001	Tamara	Wrenton	tamara.norridge2798@example.org	555-098-6227	6955 Hazelmoor Drive	Jorvport	62134	—	CONSUMER	2019-07-08	false	Prefers indigo meadow onyx; contact tamara.norridge2798@example.org
CUS00000002	Celis	Ashvane	celis.norvane6722@example.com	555-022-3867	7952 Duskwater Avenue	Aldrenvale	16645	—	CONSUMER	2020-09-26	false	Prefers jasper yonder bright sable; contact celis.norvane6722@example.com

Masking: before and after

Template Enterprise standard PII masking (GE_STANDARD_PII) applied to a synthetic Customer record from crm.customers.

Synthetic record

Synthetic Customer record before and after masking
Column	Classified as	Before (synthetic)	After masking
`customer_id`	Direct identifier	`CUS00000001`	`CUS18551589`
`first_name`	PII, Direct identifier	`Tamara`	`Ulius`
`last_name`	PII, Direct identifier	`Wrenton`	`Quarrworth`
`email`	PII, Direct identifier	`tamara.norridge2798@example.org`	`yorira.langholt7847@example.net`
`phone`	PII, Direct identifier	`555-098-6227`	`555-005-2101`
`street_address`	Quasi identifier, PII	`6955 Hazelmoor Drive`	`1160 Juniperwell Terrace`
`city`	Quasi identifier	`Jorvport`	`Neskby`
`postal_code`	Quasi identifier, PII	`62134`	`57600`
`notes`	Sensitive	`Prefers indigo meadow onyx; contact tamara.norridge2798@example.org`	`[REDACTED]`

Same pseudonym in two systems. The identifier CUS00000001 appears in crm.customers.customer_id and in commerce.orders.customer_ref. Both become CUS18551589, so the masked systems still join (relationship GE_ORDER_CUSTOMER).

Masked with a fixed public sample key so this example is reproducible; your data is masked with your own key, referenced from your secret store.

Synthetic scenarios you can run

9 runnable scenarios. Run any of them in the hosted synthetic sandbox, or preview them in your browser without an account.

Seasonal peak

High volume GE_SEASONAL_PEAK

Holiday or quarter-end peak: many online orders per customer in a six-week window

Many children per entity (load, pagination and performance paths). Children per parent record: 3–8.

Run in sandbox: Seasonal peak Preview in the browser demo: Seasonal peak

Backorders

Rare GE_BACKORDERS

Out-of-stock products and back-ordered lines awaiting replenishment

Valid but uncommon business situations that production samples often miss. Children per parent record: 0–3.

Run in sandbox: Backorders Preview in the browser demo: Backorders

Cancellations returns

Rare GE_CANCELLATIONS_RETURNS

Cancelled and returned orders with matching line statuses and refunds

Valid but uncommon business situations that production samples often miss. Children per parent record: 0–3.

Run in sandbox: Cancellations returns Preview in the browser demo: Cancellations returns

Late shipments

Rare GE_LATE_SHIPMENTS

Shipments or go-lives that missed the promised date by one to eight weeks

Valid but uncommon business situations that production samples often miss. Children per parent record: 0–3.

Run in sandbox: Late shipments Preview in the browser demo: Late shipments

Overdue receivables

Rare GE_OVERDUE_RECEIVABLES

Overdue and partially paid invoices for dunning and credit-control tests

Valid but uncommon business situations that production samples often miss. Children per parent record: 0–3.

Run in sandbox: Overdue receivables Preview in the browser demo: Overdue receivables

Contract renewals

Normal GE_CONTRACT_RENEWALS

Supplier contracts expiring within 90 days of the reference date, some set to auto-renew

Everyday, valid records: the baseline most tests expect. Children per parent record: 0–3.

Run in sandbox: Contract renewals Preview in the browser demo: Contract renewals

Supplier price changes

Boundary GE_SUPPLIER_PRICE_CHANGES

List prices at the extremes and order lines charged at stale prices after a supplier change

Values at the edges of valid ranges (limits, thresholds, extremes). Children per parent record: 0–3.

Run in sandbox: Supplier price changes Preview in the browser demo: Supplier price changes

Historical

Historical GE_HISTORICAL

Legacy records from 1995-2010 for archive, migration and retention tests

Old records for archive, migration and retention tests. Children per parent record: 0–3.

Run in sandbox: Historical Preview in the browser demo: Historical

Dirty duplicates

Duplicate GE_DIRTY_DUPLICATES

Duplicate customer and supplier records with different keys (master-data clean-up tests)

Records that repeat others under new keys (clean-up and matching tests). Children per parent record: 0–3.

Run in sandbox: Dirty duplicates Preview in the browser demo: Dirty duplicates

Negative tests, kept apart. This scenario produces deliberately broken data for error handling and never passes certification as valid data:

GE_BROKEN_REFERENCES — Orders and invoices pointing at missing customers or orders, plus invalid amounts

Sample schemas and representative outputs

The schema the pack expects in each system (also as CREATE TABLE DDL in the downloads). A run produces a masked or synthetic dataset with the same tables, a certification report with the gates of the chosen preset, and an evidence manifest with checksums — the rows stay in your environment.

Customer — crm.customers · 13 columns

A person or organisation that buys from the enterprise (B2C consumer or B2B account contact)

Column	Type	Required	Sensitive classes
`customer_id` (key)	`VARCHAR(16)`	Yes	Direct identifier
`first_name`	`VARCHAR(64)`	No	PII, Direct identifier
`last_name`	`VARCHAR(64)`	No	PII, Direct identifier
`email`	`VARCHAR(128)`	No	PII, Direct identifier
`phone`	`VARCHAR(32)`	No	PII, Direct identifier
`street_address`	`VARCHAR(128)`	No	Quasi identifier, PII
`city`	`VARCHAR(64)`	No	Quasi identifier
`postal_code`	`VARCHAR(12)`	No	Quasi identifier, PII
`company_name`	`VARCHAR(128)`	No	—
`segment`	`VARCHAR(24)`	Yes	—
`created_on`	`DATE`	Yes	—
`marketing_opt_in`	`BOOLEAN`	Yes	—
`notes`	`VARCHAR(512)`	No	Sensitive

Product — commerce.products · 8 columns

A sellable item or service plan (SKU) with list price and stock level

Column	Type	Required	Sensitive classes
`product_id` (key)	`BIGINT`	Yes	—
`sku`	`VARCHAR(24)`	Yes	—
`product_name`	`VARCHAR(128)`	Yes	—
`category`	`VARCHAR(32)`	Yes	—
`unit_price`	`DECIMAL(12,2)`	Yes	—
`supplier_ref`	`VARCHAR(16)`	No	—
`stock_on_hand`	`INTEGER`	Yes	—
`discontinued`	`BOOLEAN`	Yes	—

Order — commerce.orders · 11 columns

A customer order (sales order, web order, subscription order or shipment request)

Column	Type	Required	Sensitive classes
`order_id` (key)	`BIGINT`	Yes	—
`order_number`	`VARCHAR(24)`	Yes	—
`customer_ref`	`VARCHAR(16)`	Yes	Direct identifier
`sales_rep_ref`	`VARCHAR(16)`	No	Direct identifier
`channel`	`VARCHAR(16)`	Yes	—
`status`	`VARCHAR(16)`	Yes	—
`ordered_at`	`TIMESTAMP`	Yes	—
`promised_on`	`DATE`	Yes	—
`shipped_on`	`DATE`	No	—
`ship_to_postal_code`	`VARCHAR(12)`	No	Quasi identifier, PII
`total_amount`	`DECIMAL(12,2)`	Yes	—

OrderLine — commerce.order_lines · 6 columns

One product line of an order

Column	Type	Required	Sensitive classes
`order_line_id` (key)	`BIGINT`	Yes	—
`order_id`	`BIGINT`	Yes	—
`product_id`	`BIGINT`	Yes	—
`quantity`	`INTEGER`	Yes	—
`unit_price`	`DECIMAL(12,2)`	Yes	—
`line_status`	`VARCHAR(16)`	Yes	—

Supplier — procurement.suppliers · 8 columns

A vendor of goods or services, with a named contact and remittance bank details

Column	Type	Required	Sensitive classes
`supplier_id` (key)	`VARCHAR(16)`	Yes	—
`supplier_name`	`VARCHAR(128)`	Yes	—
`contact_name`	`VARCHAR(128)`	No	PII, Direct identifier
`contact_email`	`VARCHAR(128)`	No	PII, Direct identifier
`contact_phone`	`VARCHAR(32)`	No	PII, Direct identifier
`country_code`	`VARCHAR(2)`	Yes	—
`iban`	`VARCHAR(34)`	No	Financial
`payment_terms_days`	`INTEGER`	Yes	—

Contract — procurement.contracts · 10 columns

A supply, service or licence agreement with a supplier, owned by an employee

Column	Type	Required	Sensitive classes
`contract_id` (key)	`BIGINT`	Yes	—
`supplier_id`	`VARCHAR(16)`	Yes	—
`owner_employee_ref`	`VARCHAR(16)`	No	Direct identifier
`contract_type`	`VARCHAR(24)`	Yes	—
`start_date`	`DATE`	Yes	—
`end_date`	`DATE`	Yes	—
`annual_value`	`DECIMAL(14,2)`	Yes	Financial, Sensitive
`auto_renew`	`BOOLEAN`	Yes	—
`status`	`VARCHAR(16)`	Yes	—
`terms_summary`	`VARCHAR(512)`	No	Sensitive

Location — hr.locations · 7 columns

A site: store, warehouse, plant, office or distribution hub

Column	Type	Required	Sensitive classes
`location_id` (key)	`VARCHAR(12)`	Yes	—
`location_name`	`VARCHAR(96)`	Yes	—
`location_type`	`VARCHAR(16)`	Yes	—
`street_address`	`VARCHAR(128)`	No	Quasi identifier, PII
`city`	`VARCHAR(64)`	Yes	Quasi identifier
`postal_code`	`VARCHAR(12)`	No	Quasi identifier, PII
`country_code`	`VARCHAR(2)`	Yes	—

Employee — hr.employees · 14 columns

A worker employed at a location (sales rep, buyer, warehouse operative, engineer…)

Column	Type	Required	Sensitive classes
`employee_id` (key)	`VARCHAR(16)`	Yes	Direct identifier
`first_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`last_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`work_email`	`VARCHAR(128)`	No	PII, Direct identifier
`phone`	`VARCHAR(32)`	No	PII, Direct identifier
`birth_date`	`DATE`	No	Quasi identifier, PII
`national_id`	`VARCHAR(16)`	No	PII, Direct identifier
`hire_date`	`DATE`	Yes	—
`job_title`	`VARCHAR(64)`	Yes	—
`department`	`VARCHAR(32)`	Yes	—
`location_id`	`VARCHAR(12)`	Yes	—
`annual_salary`	`DECIMAL(12,2)`	No	Financial, Sensitive
`bank_account`	`VARCHAR(34)`	No	Financial
`employment_status`	`VARCHAR(16)`	Yes	—

Invoice — finance.invoices · 10 columns

A bill issued for an order

Column	Type	Required	Sensitive classes
`invoice_id` (key)	`BIGINT`	Yes	—
`invoice_number`	`VARCHAR(24)`	Yes	—
`order_ref`	`BIGINT`	Yes	—
`customer_ref`	`VARCHAR(16)`	Yes	Direct identifier
`billing_email`	`VARCHAR(128)`	No	PII, Direct identifier
`issued_on`	`DATE`	Yes	—
`due_on`	`DATE`	Yes	—
`amount_due`	`DECIMAL(12,2)`	Yes	—
`currency_code`	`VARCHAR(3)`	Yes	—
`status`	`VARCHAR(16)`	Yes	—

Payment — finance.payments · 7 columns

Money received against an invoice (full, partial or refunded)

Column	Type	Required	Sensitive classes
`payment_id` (key)	`BIGINT`	Yes	—
`invoice_id`	`BIGINT`	Yes	—
`paid_on`	`DATE`	Yes	—
`amount`	`DECIMAL(12,2)`	Yes	—
`method`	`VARCHAR(16)`	Yes	—
`payer_iban`	`VARCHAR(34)`	No	Financial
`remittance_note`	`VARCHAR(256)`	No	Sensitive

Compatible connectors

Verified with this pack version: PostgreSQL and Local files (Parquet / CSV). 25 more connectors are compatible by capability: they support what the pack needs, but have not been verified with this pack yet.

Connector	Status with General Enterprise
Local files (Parquet / CSV)	Verified with this pack
PostgreSQL	Verified with this pack
Amazon DynamoDB	Compatible by capability (not yet verified with this pack)
Amazon Redshift	Compatible by capability (not yet verified with this pack)
Amazon S3 / S3-compatible storage	Compatible by capability (not yet verified with this pack)
Apache Kafka	Compatible by capability (not yet verified with this pack)
Azure Blob Storage / Data Lake Storage	Compatible by capability (not yet verified with this pack)
Azure Cosmos DB	Compatible by capability (not yet verified with this pack)
Azure Synapse Analytics	Compatible by capability (not yet verified with this pack)
CockroachDB	Compatible by capability (not yet verified with this pack)
Databricks	Compatible by capability (not yet verified with this pack)
Generic SQL (SQLAlchemy)	Compatible by capability (not yet verified with this pack)
Google BigQuery	Compatible by capability (not yet verified with this pack)
HTTP APIs	Compatible by capability (not yet verified with this pack)
IBM Db2	Compatible by capability (not yet verified with this pack)
IBM Informix	Compatible by capability (not yet verified with this pack)
Mainframe files (EBCDIC / copybook)	Compatible by capability (not yet verified with this pack)
MariaDB	Compatible by capability (not yet verified with this pack)
Microsoft SQL Server	Compatible by capability (not yet verified with this pack)
MongoDB	Compatible by capability (not yet verified with this pack)
MySQL	Compatible by capability (not yet verified with this pack)
Oracle Database	Compatible by capability (not yet verified with this pack)
Parquet files	Compatible by capability (not yet verified with this pack)
Salesforce	Compatible by capability (not yet verified with this pack)
ServiceNow	Compatible by capability (not yet verified with this pack)
Snowflake	Compatible by capability (not yet verified with this pack)
SQLite (developer evaluation)	Compatible by capability (not yet verified with this pack)

Prerequisites and expected setup effort

You need

A DataNivra account; the Free Trial includes this pack.
One DataNivra agent inside your network (outbound HTTPS only) that ships General Enterprise 2.0.0.
Read-only access to a compatible source (verified with this pack: PostgreSQL, Local files (Parquet / CSV)).
A non-production target environment the agent may write test data to.
A masking key in your own secret store, referenced as vault://…, azure-kv://… or env://… (DataNivra only ever sees the reference).
For production sources, a second person who approves policies (separation of duties).

A DataNivra agent that reports its installed industry packs (releases after agent 0.3.0); the control plane runs a pack job only on an agent holding the exact active pack version with the catalogued integrity digest.

Expected setup effort (estimates)

Step	Estimate
Try the synthetic sandbox No install: sign up and open the sandbox.	Minutes (estimate)
Install (or reuse) the agent One Docker command or a Helm chart; outbound HTTPS only.	Under an hour (estimate)
Connect a source Register a read-only source through the existing connector workflow.	Under an hour (estimate)
Review and approve policies Create drafts from the General Enterprise templates, review and approve them.	Under an hour (estimate)
First certified dataset Run the first job; certification and evidence are produced automatically.	Minutes (estimate)

Certification presets in plain language

`GE_STANDARD`

All gates, full masking coverage, zero orphans, exact row counts

Masking coverage of at least 100% of sensitive columns
No orphaned child records
Empty-value ratio may rise by at most 5%
Row counts must match exactly

16 gates it requires

POLICY_COVERAGE: every sensitive column is covered by an approved policy
MASKING_COMPLETION: masking finished on every covered column
REFERENTIAL_INTEGRITY: every reference still points at an existing record
SCHEMA_VALIDATION: the output schema matches the source schema
DATA_QUALITY: empty-value ratios stay within the preset’s drift limit
ROW_COUNT_RECONCILIATION: row counts match the plan within the tolerance
ORPHAN_DETECTION: no child record lost its parent
PROVENANCE: every row is tagged masked or synthetic
MANIFEST: a manifest lists every output table with checksums
POLICY_VERSION: the exact approved policy versions are recorded
ENGINE_VERSION: the engine version is recorded
CHECKSUMS: output checksums are recorded for later verification
IDENTITY_CONSISTENCY: linked identifiers got the same pseudonym in every system
SOURCE_READ_ONLY: the source was only read, never written
EGRESS_GUARD: no row-level data left the agent
CONNECTOR_HEALTH: the connectors stayed healthy during the run

`GE_DEVELOPMENT`

All gates; tolerates slightly more NULL drift for developer sandboxes

Masking coverage of at least 100% of sensitive columns
No orphaned child records
Empty-value ratio may rise by at most 15%
Row counts must match exactly

16 gates it requires

POLICY_COVERAGE: every sensitive column is covered by an approved policy
MASKING_COMPLETION: masking finished on every covered column
REFERENTIAL_INTEGRITY: every reference still points at an existing record
SCHEMA_VALIDATION: the output schema matches the source schema
DATA_QUALITY: empty-value ratios stay within the preset’s drift limit
ROW_COUNT_RECONCILIATION: row counts match the plan within the tolerance
ORPHAN_DETECTION: no child record lost its parent
PROVENANCE: every row is tagged masked or synthetic
MANIFEST: a manifest lists every output table with checksums
POLICY_VERSION: the exact approved policy versions are recorded
ENGINE_VERSION: the engine version is recorded
CHECKSUMS: output checksums are recorded for later verification
IDENTITY_CONSISTENCY: linked identifiers got the same pseudonym in every system
SOURCE_READ_ONLY: the source was only read, never written
EGRESS_GUARD: no row-level data left the agent
CONNECTOR_HEALTH: the connectors stayed healthy during the run

`GE_INTEGRITY_FOCUS`

Privacy, integrity and provenance gates only (for quick CI smoke datasets)

Masking coverage of at least 100% of sensitive columns
No orphaned child records
Empty-value ratio may rise by at most 5%
Row counts must match exactly

8 gates it requires

POLICY_COVERAGE: every sensitive column is covered by an approved policy
MASKING_COMPLETION: masking finished on every covered column
REFERENTIAL_INTEGRITY: every reference still points at an existing record
ORPHAN_DETECTION: no child record lost its parent
PROVENANCE: every row is tagged masked or synthetic
POLICY_VERSION: the exact approved policy versions are recorded
MANIFEST: a manifest lists every output table with checksums
CHECKSUMS: output checksums are recorded for later verification

Activate it and create your first certified dataset

Seven steps in the console, each linked below. Nothing needs DataNivra staff.

Activate the pack for your organization
Activation checks your plan, the pack's integrity digest and its dependencies, then enables General Enterprise 2.0.0.
Open pack activation in the console
Install or reuse your agent
One agent runs every pack you activate; it must report this pack version. Upgrade it if it does not.
Install the agent in the console
Connect a compatible source
Start with a connector verified with this pack: PostgreSQL, Local files (Parquet / CSV).
Connect a source in the console
Create policies from the pack templates
Create drafts from Enterprise standard PII masking, Minimal-linkage analytics masking, HR and payroll strict masking; a second person approves them (separation of duties).
Create policies from templates in the console
Request your first dataset
Choose a scenario such as Seasonal peak (GE_SEASONAL_PEAK), the subset size and relationship closure.
Request a dataset in the console
Certify
The run is certified against a preset such as GE_STANDARD; a failed dataset is never provisioned.
See certification evidence in the console
Provision and keep the evidence
Provision the certified dataset to a non-production target, download the evidence and schedule refreshes.
Provision the dataset in the console

Downloads

Version 2.0.0, 63 files (183.3 KB), all synthetic and generated from the pack itself. Every file’s SHA-256 is listed in MANIFEST.json.

Start here (2)

QUICKSTART.md 2.9 KB · SHA-256 513f33a3b9ad…
TROUBLESHOOTING.md 4.7 KB · SHA-256 4b45010116e1…

Entity–relationship diagram (2)

er-diagram.mmd 4.4 KB · SHA-256 b61944ab7c8b…
er-diagram.svg 7.1 KB · SHA-256 99d7d7e3ec44…

Sample schemas (6)

schema/commerce.sql 1.8 KB · SHA-256 fa13926b890f…
schema/crm.sql 1.1 KB · SHA-256 806e84de063b…
schema/entities.json 28.8 KB · SHA-256 050c6c685364…
schema/finance.sql 1.3 KB · SHA-256 3c102209e766…
schema/hr.sql 1.8 KB · SHA-256 0eb200e74814…
schema/procurement.sql 1.5 KB · SHA-256 b980d8b957b6…

Policy templates (10)

policies/GE_ANALYTICS_MINIMAL.json 13.2 KB · SHA-256 fd2e22ead483…
policies/GE_CLASSIFICATION.json 207 B · SHA-256 ebb4cf0681ef…
policies/GE_DEVELOPMENT.json 549 B · SHA-256 a0728ad1a443…
policies/GE_HR_STRICT.json 14.2 KB · SHA-256 8b44ae37e31e…
policies/GE_INTEGRITY_FOCUS.json 354 B · SHA-256 c6f1cc4fa1c1…
policies/GE_REFRESH_WEEKLY.json 190 B · SHA-256 e54930bed540…
policies/GE_STANDARD.json 549 B · SHA-256 df479db4f4bb…
policies/GE_STANDARD_PII.json 14.2 KB · SHA-256 0f2a3359a40b…
policies/GE_SUBSET_DEFAULT.json 112 B · SHA-256 d46c49c10d7c…
policies/README.md 1.3 KB · SHA-256 8e05f6631e08…

API, CLI, SDK and CI/CD examples (10)

api/curl.sh 2.6 KB · SHA-256 925c6cc63af2…
api/dataset-request.json 1.5 KB · SHA-256 41d49b8ad0ea…
api/policy-approval.json 155 B · SHA-256 d55e632a5036…
api/policy-drafts.json 162 B · SHA-256 1e25229c4597…
api/provision.json 63 B · SHA-256 7f02a6a87792…
api/sandbox.json 46 B · SHA-256 83fd65b687cf…
ci/README.md 497 B · SHA-256 dcacfd101886…
ci/github-actions.yml 1.9 KB · SHA-256 a551ec783162…
cli.md 1.7 KB · SHA-256 7c96da675d53…
sdk_example.py 1.6 KB · SHA-256 98ae3e7e4e3e…

Synthetic sample data (CSV, JSON, Parquet) (31)

synthetic/SYNTHETIC_MANIFEST.json 2.2 KB · SHA-256 146e11052a62…
synthetic/commerce.order_lines.csv 1.4 KB · SHA-256 72675ae8365e…
synthetic/commerce.order_lines.json 6.8 KB · SHA-256 843a71d28faf…
synthetic/commerce.order_lines.parquet 2.6 KB · SHA-256 aa2d4bc39c6d…
synthetic/commerce.orders.csv 1.5 KB · SHA-256 0085b79eb28f…
synthetic/commerce.orders.json 4.6 KB · SHA-256 d7fbac5b81a9…
synthetic/commerce.orders.parquet 4.0 KB · SHA-256 53934ed4da4b…
synthetic/commerce.products.csv 711 B · SHA-256 4772be14403e…
synthetic/commerce.products.json 2.1 KB · SHA-256 c5272c3b9d06…
synthetic/commerce.products.parquet 3.0 KB · SHA-256 bf9a1e901ef4…
synthetic/crm.customers.csv 1.1 KB · SHA-256 699684be1294…
synthetic/crm.customers.json 2.4 KB · SHA-256 82855919f39e…
synthetic/crm.customers.parquet 4.8 KB · SHA-256 d19b6173cd03…
synthetic/finance.invoices.csv 1.4 KB · SHA-256 7b5430a3ce55…
synthetic/finance.invoices.json 3.8 KB · SHA-256 ada58b71cb24…
synthetic/finance.invoices.parquet 3.7 KB · SHA-256 44b8416b38f5…
synthetic/finance.payments.csv 458 B · SHA-256 9e720264a5f5…
synthetic/finance.payments.json 1.4 KB · SHA-256 08159990947c…
synthetic/finance.payments.parquet 2.7 KB · SHA-256 78c7fbfb1616…
synthetic/hr.employees.csv 1.1 KB · SHA-256 d7616e61ffe1…
synthetic/hr.employees.json 2.6 KB · SHA-256 7ee1217074dd…
synthetic/hr.employees.parquet 5.0 KB · SHA-256 cda143a7d499…
synthetic/hr.locations.csv 341 B · SHA-256 d533716137c3…
synthetic/hr.locations.json 808 B · SHA-256 fa8132d83e21…
synthetic/hr.locations.parquet 2.5 KB · SHA-256 351f002f8b46…
synthetic/procurement.contracts.csv 491 B · SHA-256 c73b2613eef7…
synthetic/procurement.contracts.json 1.3 KB · SHA-256 f9b13c6b44a6…
synthetic/procurement.contracts.parquet 3.1 KB · SHA-256 c1abe33ae00c…
synthetic/procurement.suppliers.csv 509 B · SHA-256 8c07b7be543f…
synthetic/procurement.suppliers.json 1.0 KB · SHA-256 b7def65573c5…
synthetic/procurement.suppliers.parquet 3.1 KB · SHA-256 df26a0de6ee7…

Troubleshooting

The reason codes you can meet on the way, with the recovery step. Every code is also in the error-code catalog.

SANDBOX_PACK_NOT_OFFERED — Synthetic estate not offered: A requested synthetic estate (industry pack) is not available in the hosted sandbox. What to do: Start the sandbox with the default estates.
ENTITLEMENT_REQUIRED — Plan does not include this: Your plan does not include this feature or industry pack. What to do: Upgrade in Billing & Plan.
PACK_INTEGRITY_UNVERIFIED — Pack integrity not verified: This pack version was registered without an integrity digest, so it cannot be activated (fail closed). What to do: Ask your operator to re-register the pack catalogue with the current control-plane image (register-pack), then activate again.
PACK_DEPENDENCY_INACTIVE — Required pack not active: This pack depends on another industry pack that is not active for your organization. What to do: Activate the packs this pack depends on first, then activate it again.
AGENT_PACK_MISSING — No agent can run this industry pack: The job's agent does not report the pack (older agents report no packs at all), so the job was not sent. What to do: Upgrade the agent to a release that reports its installed packs and ships this pack, then run the request again.
AGENT_PACK_VERSION_INCOMPATIBLE — Industry pack version differs on the agent: The agent holds a different version of the pack than the one the job was approved for. What to do: Deploy an agent with the pack's active version, or roll the pack back in Industry packs.
AGENT_PACK_INTEGRITY_MISMATCH — Agent pack differs from the catalogue: The agent reports the pack's version with a different integrity digest than the catalogued one, so the job was not sent. What to do: Redeploy the agent from the official signed image for this release, then run the request again.
PACK_TEMPLATES_UNAVAILABLE — Pack templates not registered: This pack version was registered without its policy templates. What to do: Ask your operator to re-register the pack catalogue (register-pack); create policies manually meanwhile.
PACK_TEMPLATE_KEY_REF_REQUIRED — Masking key reference required: A selected template keeps identities linked across systems and needs your masking key reference. What to do: Provide key_ref, e.g. vault://your-vault/tdm-masking-key, then create the drafts again.
PACK_NOT_ENABLED — Industry pack not enabled: The industry pack is not enabled for this tenant. What to do: Enable the pack (if your plan includes it).

Frequently asked questions

Are the General Enterprise records on this page real?: No. Every record on this page is synthetic, generated from a fixed seed by the pack's own generator; masked values come from the real masking engine. The masking example uses a fixed public sample key; your own data is masked with a key from your secret store.
Can I activate the General Enterprise pack myself?: Included in Free Trial, Developer, Team, Business, Enterprise and Private Cloud / Self-Hosted. You can activate it for your organization yourself in the console; no call is needed.
Which databases and files does the General Enterprise pack work with?: Verified with this pack version: PostgreSQL and Local files (Parquet / CSV). 25 more connectors are compatible by capability: they support what the pack needs, but have not been verified with this pack yet.
How long does a first certified General Enterprise dataset take?: Estimates, not guarantees — try the synthetic sandbox: minutes; install (or reuse) the agent: under an hour; connect a source: under an hour; review and approve policies: under an hour; first certified dataset: minutes.
Which test scenarios does the General Enterprise pack include?: 9 runnable scenarios (seasonal peak, backorders, cancellations returns, late shipments and more), plus 1 negative-test scenario kept apart from valid data.
Do General Enterprise rows leave my network?: No. The DataNivra agent runs inside your environment: it reads the source, masks, subsets, generates and certifies there, and sends only metadata, aggregate counts and evidence to the DataNivra control plane (customer-resident processing, zero raw-production-data egress).

Learn the concepts, then come back to activate

Regulatory context

Supports GDPR-oriented data-minimisation practices by keeping personal data in your environment. It does not by itself establish compliance.

Try with synthetic data Activate this pack — start free

No call and no credit card needed. Prefer a conversation? Request a demo (optional). Missing something? Request a feature.

Problems this pack solves

Masked data that still joins across 5 systems

The cases production samples rarely contain

Sensitive fields found and masked before anyone sees them

Evidence that each dataset is fit to use

Entities and relationships

Realistic synthetic records

Masking: before and after

Synthetic scenarios you can run

Seasonal peak

Backorders

Cancellations returns

Late shipments

Overdue receivables

Contract renewals

Supplier price changes

Historical

Dirty duplicates

Sample schemas and representative outputs

Compatible connectors

Prerequisites and expected setup effort

You need

Expected setup effort (estimates)

Certification presets in plain language

GE_STANDARD

GE_DEVELOPMENT

GE_INTEGRITY_FOCUS

Activate it and create your first certified dataset

Activate the pack for your organization

Install or reuse your agent

Connect a compatible source

Create policies from the pack templates

Request your first dataset

Certify

Provision and keep the evidence

Downloads

Troubleshooting

Frequently asked questions

Learn the concepts, then come back to activate

Regulatory context

`GE_STANDARD`

`GE_DEVELOPMENT`

`GE_INTEGRITY_FOCUS`