Are the Education 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 Education 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 Education pack include?

11 runnable scenarios (term in progress, mid term withdrawals, grade appeals, late submissions and more), plus 1 negative-test scenario kept apart from valid data.

Do Education 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

Education industry pack

Synthetic student, enrolment, transcript, LMS and student-finance test data with linked masking

Preview only · not activatable yet Version 1.0.0 · Complete

Try with synthetic data Notify me — tell us you need it

Everything on this page works without an account. Prefer a conversation? Request a demo (optional). Missing something? Request a feature.

This pack is installed and passes DataNivra’s pack conformance kit through the real engine, but it cannot be activated yet: no plan includes it yet, the standard agent image does not ship it, the control-plane catalogue does not list it and the hosted sandbox has no synthetic estate for it. You can explore its synthetic records, masking and scenarios on this page and in the browser demo. Tell us if you need it: demand decides which packs become activatable next.

Problems this pack solves

Masked data that still joins across 4 systems

7 cross-system relationships link finance, identity, lms and sis; the pack's masking keeps one pseudonym per identity on every side. For example, sis.students.student_id and identity.student_accounts.student_ref get the same pseudonym.

The cases production samples rarely contain

9 ready-made scenarios generate them on demand, for example: enrolments withdrawn mid-term with a withdrawal date and a W grade earning no credit; grades changed after an appeal or a clerical correction, keeping superseded transcript rows; assignments submitted between one minute and three days after their due time.

Sensitive fields found and masked before anyone sees them

41 columns across 12 entities are classified (credential, 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

2 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

12 entities across 4 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 12 relationships as a table

Relationships of the Education pack
Entity	References	Columns	Kind
Guardian	Student	`student_id` → `student_id`	Within a system
Section	Course	`course_code` → `course_code`	Within a system
Enrolment	Student	`student_id` → `student_id`	Within a system
Enrolment	Section	`section_id` → `section_id`	Within a system
Grade	Enrolment	`enrolment_id` → `enrolment_id`	Within a system
Section	StaffMember	`instructor_ref` → `staff_id`	Across systems (pack relationship template)
StudentAccount	Student	`student_ref` → `student_id`	Across systems (pack relationship template)
Submission	Student	`learner_ref` → `student_id`	Across systems (pack relationship template)
Submission	Section	`section_ref` → `section_id`	Across systems (pack relationship template)
ActivityEvent	Student	`learner_ref` → `student_id`	Across systems (pack relationship template)
TuitionCharge	Student	`student_ref` → `student_id`	Across systems (pack relationship template)
AidAward	Student	`student_ref` → `student_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

Student — `sis.students` (synthetic)
`student_id` sensitive	`given_name` sensitive	`family_name` sensitive	`preferred_name` sensitive	`birth_date` sensitive	`national_id` sensitive	`email` sensitive	`phone` sensitive	`street_address` sensitive	`city` sensitive	`postal_code` sensitive	`country_code`	`programme_level`	`cohort_year`	`status`	`accommodation_code` sensitive	`directory_opt_out`
STU000000001	Joo	Eldcombe	—	2010-05-18	—	joo.dunton5508@example.net	—	5214 Zephyrine Walk	Kelmhaven	86043	ZZ	SECONDARY	2024	ACTIVE	—	true
STU000000002	Doren	Yarrvane	—	1992-01-28	NID-ZZ-0449730	doren.iverby7986@example.org	555-086-3379	7841 Silverlark Way	Kelmton	24003	ZZ	UNDERGRADUATE	2025	ACTIVE	—	false

Masking: before and after

Template Linked education test data (EDU_LINKED_TEST_DATA) applied to a synthetic Student record from sis.students.

Synthetic record

Synthetic Student record before and after masking
Column	Classified as	Before (synthetic)	After masking
`student_id`	Direct identifier	`STU000000001`	`STU307535978`
`given_name`	PII, Direct identifier	`Joo`	`Wilira`
`family_name`	PII, Direct identifier	`Eldcombe`	`Caldby`
`preferred_name`	PII, Direct identifier	`—`	`—` (same value after masking)
`birth_date`	PII, Quasi identifier	`2010-05-18`	`2010-06-01`
`national_id`	PII, Direct identifier	`—`	`—` (same value after masking)
`email`	PII, Direct identifier	`joo.dunton5508@example.net`	`vesyn.bramby9065@example.com`
`phone`	PII, Direct identifier	`—`	`—` (same value after masking)
`street_address`	PII, Quasi identifier	`5214 Zephyrine Walk`	`3171 Quillon Terrace`
`city`	Quasi identifier	`Kelmhaven`	`Farrowwick`
`postal_code`	PII, Quasi identifier	`86043`	`77785`
`accommodation_code`	Sensitive	`—`	`—` (same value after masking)

Same pseudonym in two systems. The identifier STU000000001 appears in sis.students.student_id and in identity.student_accounts.student_ref. Both become STU307535978, so the masked systems still join (relationship EDU_ACCOUNT_STUDENT).

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

11 runnable scenarios. Preview them in your browser without an account; the hosted sandbox opens when the pack becomes activatable.

Term in progress

Normal EDU_TERM_IN_PROGRESS

Ordinary learners, guardians, enrolments, grades, LMS work, activity, charges and aid

Everyday, valid records: the baseline most tests expect. Children per parent record: 1–3.

Preview in the browser demo: Term in progress

Mid term withdrawals

Rare EDU_MID_TERM_WITHDRAWALS

Enrolments withdrawn mid-term with a withdrawal date and a W grade earning no credit

Valid but uncommon business situations that production samples often miss. Children per parent record: 1–3.

Preview in the browser demo: Mid term withdrawals

Grade appeals

Rare EDU_GRADE_APPEALS

Grades changed after an appeal or a clerical correction, keeping superseded transcript rows

Valid but uncommon business situations that production samples often miss. Children per parent record: 2–3.

Preview in the browser demo: Grade appeals

Late submissions

Rare EDU_LATE_SUBMISSIONS

Assignments submitted between one minute and three days after their due time

Valid but uncommon business situations that production samples often miss. Children per parent record: 2–5.

Preview in the browser demo: Late submissions

Payment plans and aid

Rare EDU_PAYMENT_PLANS_AND_AID

Charges on payment plans or overdue, and disbursed aid for lower household-income bands

Valid but uncommon business situations that production samples often miss. Children per parent record: 1–3.

Preview in the browser demo: Payment plans and aid

Accommodations

Rare EDU_ACCOMMODATIONS

Every learner has a learning-support accommodation code (special-category-like data)

Valid but uncommon business situations that production samples often miss. Children per parent record: 1–2.

Preview in the browser demo: Accommodations

Safeguarding restrictions

Rare EDU_SAFEGUARDING_RESTRICTIONS

Secondary-school minors whose guardians carry no-contact or court-order restrictions

Valid but uncommon business situations that production samples often miss. Children per parent record: 1–3.

Preview in the browser demo: Safeguarding restrictions

Age of majority

Boundary EDU_AGE_OF_MAJORITY

Birth dates exactly at (every fifth row) or within three days of the 18th birthday on the census date

Values at the edges of valid ranges (limits, thresholds, extremes). Children per parent record: 1–2.

Preview in the browser demo: Age of majority

Pass mark scores

Boundary EDU_PASS_MARK_SCORES

Assessment scores exactly at the pass mark or one hundredth of a point either side of it

Values at the edges of valid ranges (limits, thresholds, extremes). Children per parent record: 1–3.

Preview in the browser demo: Pass mark scores

Duplicate lms events

Duplicate EDU_DUPLICATE_LMS_EVENTS

LMS activity events delivered twice by an integration with identical content under new ids

Records that repeat others under new keys (clean-up and matching tests). Children per parent record: 2–5.

Preview in the browser demo: Duplicate lms events

Transcript histories

Historical EDU_TRANSCRIPT_HISTORIES

Multi-year, referentially intact transcripts, LMS activity and student-finance histories

Old records for archive, migration and retention tests. Children per parent record: 2–4.

Preview in the browser demo: Transcript histories

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

EDU_BROKEN_REFERENCES — Dangling cross-system references and invalid values for error-handling tests

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.

Student — sis.students · 17 columns

A learner (the natural subset root); SECONDARY learners are minors.

Column	Type	Required	Sensitive classes
`student_id` (key)	`VARCHAR(12)`	Yes	Direct identifier
`given_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`family_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`preferred_name`	`VARCHAR(64)`	No	PII, Direct identifier
`birth_date`	`DATE`	Yes	PII, Quasi identifier
`national_id`	`VARCHAR(16)`	No	PII, Direct identifier
`email`	`VARCHAR(128)`	No	PII, Direct identifier
`phone`	`VARCHAR(32)`	No	PII, Direct identifier
`street_address`	`VARCHAR(128)`	No	PII, Quasi identifier
`city`	`VARCHAR(64)`	No	Quasi identifier
`postal_code`	`VARCHAR(10)`	No	PII, Quasi identifier
`country_code`	`VARCHAR(2)`	Yes	—
`programme_level`	`VARCHAR(16)`	Yes	—
`cohort_year`	`INT`	Yes	—
`status`	`VARCHAR(12)`	Yes	—
`accommodation_code`	`VARCHAR(12)`	No	Sensitive
`directory_opt_out`	`BOOLEAN`	Yes	—

Guardian — sis.guardians · 9 columns

A parent, guardian or carer (always present for minors; an emergency contact for some adults).

Column	Type	Required	Sensitive classes
`guardian_id` (key)	`BIGINT`	Yes	—
`student_id`	`VARCHAR(12)`	Yes	Direct identifier
`given_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`family_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`relationship`	`VARCHAR(12)`	Yes	—
`email`	`VARCHAR(128)`	No	PII, Direct identifier
`phone`	`VARCHAR(32)`	No	PII, Direct identifier
`contact_restriction`	`VARCHAR(16)`	No	Sensitive
`is_emergency_contact`	`BOOLEAN`	Yes	—

Course — sis.courses · 4 columns

A course in the catalogue (invented titles).

Column	Type	Required	Sensitive classes
`course_code` (key)	`VARCHAR(10)`	Yes	—
`title`	`VARCHAR(64)`	Yes	—
`department`	`VARCHAR(16)`	Yes	—
`credits`	`INT`	Yes	—

Section — sis.sections · 6 columns

One offering of a course in a term, taught by a member of staff.

Column	Type	Required	Sensitive classes
`section_id` (key)	`BIGINT`	Yes	—
`course_code`	`VARCHAR(10)`	Yes	—
`term_code`	`VARCHAR(6)`	Yes	—
`instructor_ref`	`VARCHAR(10)`	Yes	Direct identifier
`capacity`	`INT`	Yes	—
`modality`	`VARCHAR(10)`	Yes	—

Enrolment — sis.enrolments · 6 columns

A student registered in a section.

Column	Type	Required	Sensitive classes
`enrolment_id` (key)	`BIGINT`	Yes	—
`student_id`	`VARCHAR(12)`	Yes	Direct identifier
`section_id`	`BIGINT`	Yes	—
`status`	`VARCHAR(10)`	Yes	—
`enrolled_on`	`DATE`	Yes	—
`withdrawn_on`	`DATE`	No	—

Grade — sis.grades · 7 columns

A transcript grade of an enrolment; a changed grade keeps the superseded row (is_current false).

Column	Type	Required	Sensitive classes
`grade_id` (key)	`BIGINT`	Yes	—
`enrolment_id`	`BIGINT`	Yes	—
`grade`	`VARCHAR(2)`	Yes	Sensitive
`credits_earned`	`INT`	Yes	—
`posted_on`	`DATE`	Yes	—
`change_reason`	`VARCHAR(20)`	No	—
`is_current`	`BOOLEAN`	Yes	—

StaffMember — identity.staff · 7 columns

A teacher, lecturer, advisor or administrator in the directory.

Column	Type	Required	Sensitive classes
`staff_id` (key)	`VARCHAR(10)`	Yes	Direct identifier
`given_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`family_name`	`VARCHAR(64)`	Yes	PII, Direct identifier
`work_email`	`VARCHAR(128)`	Yes	PII, Direct identifier
`role`	`VARCHAR(12)`	Yes	—
`department`	`VARCHAR(16)`	Yes	—
`safeguarding_check`	`VARCHAR(10)`	Yes	Sensitive

StudentAccount — identity.student_accounts · 7 columns

A learner's directory login (username reuses the student number in another format).

Column	Type	Required	Sensitive classes
`account_id` (key)	`BIGINT`	Yes	—
`student_ref`	`VARCHAR(12)`	Yes	Direct identifier
`username`	`VARCHAR(12)`	Yes	Direct identifier
`password_hash`	`VARCHAR(80)`	No	Credential
`mfa_enrolled`	`BOOLEAN`	Yes	—
`last_login_at`	`TIMESTAMP`	No	—
`status`	`VARCHAR(10)`	Yes	—

Submission — lms.submissions · 9 columns

An assignment submission with its score and instructor feedback.

Column	Type	Required	Sensitive classes
`submission_id` (key)	`BIGINT`	Yes	—
`learner_ref`	`VARCHAR(12)`	Yes	Direct identifier
`section_ref`	`BIGINT`	Yes	—
`assignment_code`	`VARCHAR(10)`	Yes	—
`due_at`	`TIMESTAMP`	Yes	—
`submitted_at`	`TIMESTAMP`	Yes	—
`is_late`	`BOOLEAN`	Yes	—
`score`	`DECIMAL(5,2)`	No	Sensitive
`feedback`	`VARCHAR(400)`	No	Sensitive

ActivityEvent — lms.activity_events · 6 columns

One learner interaction in the LMS (behavioural, location-like via the client address).

Column	Type	Required	Sensitive classes
`event_id` (key)	`BIGINT`	Yes	—
`learner_ref`	`VARCHAR(12)`	Yes	Direct identifier
`event_type`	`VARCHAR(14)`	Yes	—
`occurred_at`	`TIMESTAMP`	Yes	Quasi identifier
`client_ip`	`VARCHAR(45)`	No	PII, Quasi identifier
`duration_seconds`	`INT`	Yes	—

TuitionCharge — finance.tuition_charges · 8 columns

A tuition, housing or fee charge on a student's account.

Column	Type	Required	Sensitive classes
`charge_id` (key)	`BIGINT`	Yes	—
`student_ref`	`VARCHAR(12)`	Yes	Direct identifier
`term_code`	`VARCHAR(6)`	Yes	—
`charge_type`	`VARCHAR(8)`	Yes	—
`amount`	`DECIMAL(10,2)`	Yes	Financial
`due_date`	`DATE`	Yes	—
`status`	`VARCHAR(12)`	Yes	—
`payment_reference`	`VARCHAR(20)`	No	Financial, Direct identifier

AidAward — finance.aid_awards · 8 columns

A grant, loan, scholarship or work-study award, assessed against household income.

Column	Type	Required	Sensitive classes
`award_id` (key)	`BIGINT`	Yes	—
`student_ref`	`VARCHAR(12)`	Yes	Direct identifier
`aid_year`	`VARCHAR(9)`	Yes	—
`aid_type`	`VARCHAR(12)`	Yes	—
`amount`	`DECIMAL(10,2)`	Yes	Financial
`household_income_band`	`VARCHAR(8)`	No	Financial, Sensitive
`status`	`VARCHAR(10)`	Yes	—
`disbursed_on`	`DATE`	No	—

Compatible connectors

Verified with this pack version: PostgreSQL and Local files (Parquet / CSV). 17 more connectors are compatible by capability: they support what the pack needs, but have not been verified with this pack yet.

Connector	Status with Education
Local files (Parquet / CSV)	Verified with this pack
PostgreSQL	Verified with this pack
Amazon S3 / S3-compatible storage	Compatible by capability (not yet verified with this pack)
Apache Kafka (connector in preview)	Compatible by capability (not yet verified with this pack)
Azure Blob Storage / Data Lake Storage	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 (connector in preview)	Compatible by capability (not yet verified with this pack)
IBM Db2 (connector in preview)	Compatible by capability (not yet verified with this pack)
Mainframe files (EBCDIC / copybook) (connector in preview)	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 (connector in preview)	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)
Snowflake	Compatible by capability (not yet verified with this pack)
SQLite (developer evaluation) (connector in preview)	Compatible by capability (not yet verified with this pack)

Prerequisites and expected setup effort

You need

This pack is not in any plan yet.
One DataNivra agent inside your network (outbound HTTPS only) that ships Education 1.0.0.
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 Education 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

`EDU_STRICT`

Every gate; full masking coverage; zero orphaned guardians, enrolments, grades, accounts, submissions, activity events, charges or aid awards; exact row counts. Default for masked student-record test data.

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

`EDU_SCENARIO_TESTING`

Every gate, with slightly relaxed NULL-ratio drift for scenario datasets whose business states (withdrawals without grades, single-sign-on accounts without password hashes, undisbursed aid) leave optional fields empty.

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

Activation status

What is missing before you can activate the Education pack yourself:

no plan includes it yet
the standard agent image does not ship it
the control-plane catalogue does not list it
the hosted sandbox has no synthetic estate for it

Until then, preview it in the browser demo and download its synthetic asset bundle below.

Tell us you need the Education pack or request a feature for it.

Downloads

Version 1.0.0, 67 files (238.2 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 7f2d99843c0d…
TROUBLESHOOTING.md 4.7 KB · SHA-256 94a8b36f1f35…

Entity–relationship diagram (2)

er-diagram.mmd 4.6 KB · SHA-256 55227a512e4a…
er-diagram.svg 8.1 KB · SHA-256 2b9bb28c6f7d…

Sample schemas (5)

schema/entities.json 29.8 KB · SHA-256 313f10c1b55e…
schema/finance.sql 1.3 KB · SHA-256 d0436282bda8…
schema/identity.sql 1.3 KB · SHA-256 cc5b8c9a883f…
schema/lms.sql 1.3 KB · SHA-256 9dece57ffcdc…
schema/sis.sql 3.7 KB · SHA-256 93e31d3fa9b6…

Policy templates (9)

policies/EDU_CLASSIFICATION.json 203 B · SHA-256 e29a5f54d3c5…
policies/EDU_LINKED_TEST_DATA.json 16.9 KB · SHA-256 c2110825baa2…
policies/EDU_REFRESH_WEEKLY.json 190 B · SHA-256 e54930bed540…
policies/EDU_RESEARCH_EXTRACT.json 16.2 KB · SHA-256 21c05d5dd733…
policies/EDU_SCENARIO_TESTING.json 549 B · SHA-256 a0728ad1a443…
policies/EDU_STRICT.json 549 B · SHA-256 df479db4f4bb…
policies/EDU_STUDENT_RECORDS_MINIMISED.json 17.8 KB · SHA-256 15d6725c4209…
policies/EDU_SUBSET_DEFAULT.json 112 B · SHA-256 d46c49c10d7c…
policies/README.md 1.2 KB · SHA-256 9aa8add75550…

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

api/curl.sh 2.5 KB · SHA-256 ba40502cea70…
api/dataset-request.json 1.5 KB · SHA-256 75eb5a24e897…
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 37 B · SHA-256 aa4eed793f7e…
ci/README.md 479 B · SHA-256 6cd79a1b3aa8…
ci/github-actions.yml 1.8 KB · SHA-256 8a1107ee431c…
cli.md 1.7 KB · SHA-256 b42c608f958f…
sdk_example.py 1.6 KB · SHA-256 0b24f9c167c3…

Synthetic sample data (CSV, JSON, Parquet) (37)

synthetic/SYNTHETIC_MANIFEST.json 2.6 KB · SHA-256 ec55c2625e0f…
synthetic/finance.aid_awards.csv 608 B · SHA-256 6d28e4322e41…
synthetic/finance.aid_awards.json 1.9 KB · SHA-256 08945442cfcf…
synthetic/finance.aid_awards.parquet 2.8 KB · SHA-256 b2717f62f6b4…
synthetic/finance.tuition_charges.csv 1.3 KB · SHA-256 7e09a62c43d8…
synthetic/finance.tuition_charges.json 4.3 KB · SHA-256 969d602607d2…
synthetic/finance.tuition_charges.parquet 3.0 KB · SHA-256 4fb10586f4b3…
synthetic/identity.staff.csv 825 B · SHA-256 32d5a2a36b8c…
synthetic/identity.staff.json 2.1 KB · SHA-256 813595ea44e0…
synthetic/identity.staff.parquet 2.8 KB · SHA-256 e27659192171…
synthetic/identity.student_accounts.csv 529 B · SHA-256 b49425c3f5db…
synthetic/identity.student_accounts.json 1.3 KB · SHA-256 4d585c32965f…
synthetic/identity.student_accounts.parquet 2.5 KB · SHA-256 028386c338d1…
synthetic/lms.activity_events.csv 2.0 KB · SHA-256 e525ae72e7a9…
synthetic/lms.activity_events.json 6.2 KB · SHA-256 1b95dddf112f…
synthetic/lms.activity_events.parquet 2.9 KB · SHA-256 d23077b95b7c…
synthetic/lms.submissions.csv 7.7 KB · SHA-256 298b52f77de7…
synthetic/lms.submissions.json 18.9 KB · SHA-256 96aa7bd6d807…
synthetic/lms.submissions.parquet 6.0 KB · SHA-256 a7c284c5b2d7…
synthetic/sis.courses.csv 460 B · SHA-256 de5fffc02691…
synthetic/sis.courses.json 1.4 KB · SHA-256 cec506658f5e…
synthetic/sis.courses.parquet 1.8 KB · SHA-256 3943d16092af…
synthetic/sis.enrolments.csv 1.5 KB · SHA-256 c28888f9e118…
synthetic/sis.enrolments.json 5.7 KB · SHA-256 cca14cfee19e…
synthetic/sis.enrolments.parquet 2.6 KB · SHA-256 c1de422bee0d…
synthetic/sis.grades.csv 852 B · SHA-256 566629f26d96…
synthetic/sis.grades.json 4.2 KB · SHA-256 0632bdd6d481…
synthetic/sis.grades.parquet 2.6 KB · SHA-256 2a542ecdf043…
synthetic/sis.guardians.csv 325 B · SHA-256 0943fa0a200c…
synthetic/sis.guardians.json 663 B · SHA-256 3791a1307a51…
synthetic/sis.guardians.parquet 3.0 KB · SHA-256 659d9ed20b96…
synthetic/sis.sections.csv 2.0 KB · SHA-256 42c55c195eb8…
synthetic/sis.sections.json 7.8 KB · SHA-256 0f8ecacf60a9…
synthetic/sis.sections.parquet 2.5 KB · SHA-256 d6bd68f596c9…
synthetic/sis.students.csv 1.1 KB · SHA-256 b01dc86f645c…
synthetic/sis.students.json 2.8 KB · SHA-256 34ceda13a952…
synthetic/sis.students.parquet 5.4 KB · SHA-256 026367a7443f…

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 Education 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 Education pack myself?: This pack is installed and passes DataNivra’s pack conformance kit through the real engine, but it cannot be activated yet: no plan includes it yet, the standard agent image does not ship it, the control-plane catalogue does not list it and the hosted sandbox has no synthetic estate for it. You can explore its synthetic records, masking and scenarios on this page and in the browser demo. Tell us if you need it: demand decides which packs become activatable next.
Which databases and files does the Education pack work with?: Verified with this pack version: PostgreSQL and Local files (Parquet / CSV). 17 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 Education 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 Education pack include?: 11 runnable scenarios (term in progress, mid term withdrawals, grade appeals, late submissions and more), plus 1 negative-test scenario kept apart from valid data.
Do Education 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

The Education pack covers data that laws and industry rules often treat as sensitive. It supports your privacy and governance programmes by keeping those records inside your environment and producing certification evidence; it does not, by itself, make any system or organisation compliant with any law, regulation or standard.

Try with synthetic data Notify me — tell us you need it

Everything on this page works without an account. Prefer a conversation? Request a demo (optional). Missing something? Request a feature.