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Industry packs

Travel, Airlines & Hospitality industry pack

Synthetic booking, check-in, hotel, loyalty and payment test data with linked masking

Preview only · not activatable yet Version 1.0.0 · Complete

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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 5 systems

8 cross-system relationships link departure_control, loyalty, payments, property and reservation; the pack's masking keeps one pseudonym per identity on every side. For example, reservation.travellers.traveller_id and departure_control.travel_documents.traveller_ref get the same pseudonym.

The cases production samples rarely contain

11 ready-made scenarios generate them on demand, for example: one oversold flight: confirmed passengers denied boarding or offloaded at the gate; cancelled segments after a disruption, exchanged tickets and rebooked passengers; wheelchair and medical-clearance requests with agent notes (health-revealing data).

Sensitive fields found and masked before anyone sees them

45 columns across 12 entities are classified (direct identifier, financial, payment, PHI, 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 5 source systems, generated from the pack’s own entity model.

Entity graph of the Travel, Airlines & Hospitality pack12 entities in 5 systems (reservation, departure_control, property, loyalty, payments) linked by 17 relationships, 8 of them across systems. The table after the graph lists every relationship.reservationdeparture_controlpropertyloyaltypaymentsTraveller (reservation.travellers)Travellerreservation.travellersBooking (reservation.bookings)Bookingreservation.bookingsTicket (reservation.tickets)Ticketreservation.ticketsFlightSegment (reservation.flight_segments)FlightSegmentreservation.flight_seg…SpecialServiceRequest (reservation.special_service_requests)SpecialServiceReque…reservation.special_se…TravelDocument (departure_control.travel_documents)TravelDocumentdeparture_control.trav…CheckIn (departure_control.check_ins)CheckIndeparture_control.chec…HotelStay (property.hotel_stays)HotelStayproperty.hotel_staysFolioCharge (property.folio_charges)FolioChargeproperty.folio_chargesLoyaltyMember (loyalty.loyalty_members)LoyaltyMemberloyalty.loyalty_membersLoyaltyActivity (loyalty.loyalty_activities)LoyaltyActivityloyalty.loyalty_activi…Payment (payments.payments)Paymentpayments.payments
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 17 relationships as a table
Relationships of the Travel, Airlines & Hospitality pack
EntityReferencesColumnsKind
BookingTravellerbooker_id → traveller_idWithin a system
TicketBookingpnr_locator → pnr_locatorWithin a system
TicketTravellertraveller_id → traveller_idWithin a system
FlightSegmentTicketticket_number → ticket_numberWithin a system
SpecialServiceRequestBookingpnr_locator → pnr_locatorWithin a system
SpecialServiceRequestTravellertraveller_id → traveller_idWithin a system
CheckInTravelDocumentdocument_id → document_idWithin a system
FolioChargeHotelStaystay_id → stay_idWithin a system
LoyaltyActivityLoyaltyMembermember_number → member_numberWithin a system
TravelDocumentTravellertraveller_ref → traveller_idAcross systems (pack relationship template)
CheckInTicketticket_ref → ticket_numberAcross systems (pack relationship template)
CheckInFlightSegmentsegment_ref → segment_idAcross systems (pack relationship template)
HotelStayTravellerguest_ref → traveller_idAcross systems (pack relationship template)
HotelStayLoyaltyMemberloyalty_ref → member_numberAcross systems (pack relationship template)
LoyaltyMemberTravellertraveller_ref → traveller_idAcross systems (pack relationship template)
PaymentBookingpnr_ref → pnr_locatorAcross systems (pack relationship template)
PaymentHotelStaystay_confirmation_ref → confirmation_numberAcross 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.

Traveller — reservation.travellers (synthetic)
traveller_id sensitivegiven_name sensitivefamily_name sensitivebirth_date sensitivegender sensitiveemail sensitivephone sensitiveresidence_countrycreated_on
TP000000001VesenQuarrcombe1977-06-06Mvesen.rookley36@example.net555-036-4300ZZ2017-04-27
TP000000002BraiaWrenwick1940-08-27—braia.ashton5309@example.org555-009-5153XA2020-09-18

Masking: before and after

Template Linked travel and hospitality test data (TRV_LINKED_TEST_DATA) applied to a synthetic Traveller record from reservation.travellers.

Synthetic Traveller record before and after masking
ColumnClassified asBefore (synthetic)After masking
traveller_idDirect identifierTP000000001TP098977906
given_namePII, Direct identifierVesenAlric
family_namePII, Direct identifierQuarrcombeKestfield
birth_datePII, Quasi identifier1977-06-061977-05-26
genderQuasi identifierMS
emailPII, Direct identifiervesen.rookley36@example.netneria.stowshaw3029@example.com
phonePII, Direct identifier555-036-4300055-958-5099

Same pseudonym in two systems. The identifier TP000000001 appears in reservation.travellers.traveller_id and in departure_control.travel_documents.traveller_ref. Both become TP098977906, so the masked systems still join (relationship TRV_DOCUMENT_TRAVELLER).

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

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

Everyday travel

Normal TRV_EVERYDAY_TRAVEL

Ordinary travellers, bookings, tickets, flights, check-ins, hotel stays, loyalty and payments

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

Preview in the browser demo: Everyday travel

Overbooked flight

Rare TRV_OVERBOOKED_FLIGHT

One oversold flight: confirmed passengers denied boarding or offloaded at the gate

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

Preview in the browser demo: Overbooked flight

Irregular operations

Rare TRV_IRREGULAR_OPERATIONS

Cancelled segments after a disruption, exchanged tickets and rebooked passengers

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

Preview in the browser demo: Irregular operations

Special assistance

Rare TRV_SPECIAL_ASSISTANCE

Wheelchair and medical-clearance requests with agent notes (health-revealing data)

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

Preview in the browser demo: Special assistance

Hotel no shows

Rare TRV_HOTEL_NO_SHOWS

Hotel guests who never arrive; the folio carries a no-show fee

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

Preview in the browser demo: Hotel no shows

Chargebacks

Rare TRV_CHARGEBACKS

Card payments disputed by the cardholder, with a chargeback reason

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

Preview in the browser demo: Chargebacks

Expired documents

Rare TRV_EXPIRED_DOCUMENTS

Passports and identity cards that expired before the flight (check-in must refuse them)

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

Preview in the browser demo: Expired documents

Elite loyalty

Rare TRV_ELITE_LOYALTY

Top-tier loyalty members with large points balances and frequent redemptions

Valid but uncommon business situations that production samples often miss. Children per parent record: 1–4.

Preview in the browser demo: Elite loyalty

Long haul boundary

Boundary TRV_LONG_HAUL_BOUNDARY

Flight segments at or above the long-haul block time (exactly at it for every fourth row)

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

Preview in the browser demo: Long haul boundary

Baggage allowance

Boundary TRV_BAGGAGE_ALLOWANCE

Checked bags exactly at the free allowance and one above it (excess baggage)

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

Preview in the browser demo: Baggage allowance

Journey timeline

Temporal sequence TRV_JOURNEY_TIMELINE

Booking, ticket issue, coupon, check-in and payment times follow each other along every journey

Events in a realistic order over time (sequences and state changes). Children per parent record: 1–3.

Preview in the browser demo: Journey timeline

Duplicate segments

Duplicate TRV_DUPLICATE_SEGMENTS

Duplicate bookings: the same flight coupon (flight, date, route, cabin) repeated under new ids

Records that repeat others under new keys (clean-up and matching tests). Children per parent record: 2–4.

Preview in the browser demo: Duplicate segments

Travel histories

Historical TRV_TRAVEL_HISTORIES

Multi-year, referentially intact travel, stay and loyalty histories without gaps

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

Preview in the browser demo: Travel histories

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

  • TRV_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.

Traveller — reservation.travellers · 9 columns

A traveller profile: the person who books, flies and stays (the natural subset root).

ColumnTypeRequiredSensitive classes
traveller_id (key)VARCHAR(11)YesDirect identifier
given_nameVARCHAR(64)YesPII, Direct identifier
family_nameVARCHAR(64)YesPII, Direct identifier
birth_dateDATENoPII, Quasi identifier
genderVARCHAR(1)NoQuasi identifier
emailVARCHAR(128)NoPII, Direct identifier
phoneVARCHAR(32)NoPII, Direct identifier
residence_countryVARCHAR(2)Yes—
created_onDATEYes—
Booking — reservation.bookings · 7 columns

A booking record (PNR) identified by a six-character record locator.

ColumnTypeRequiredSensitive classes
pnr_locator (key)VARCHAR(6)YesDirect identifier
booker_idVARCHAR(11)YesDirect identifier
created_atTIMESTAMPYes—
channelVARCHAR(12)Yes—
statusVARCHAR(10)Yes—
contact_emailVARCHAR(128)NoPII, Direct identifier
total_fareDECIMAL(10,2)YesFinancial
Ticket — reservation.tickets · 7 columns

An electronic ticket for one passenger of a booking.

ColumnTypeRequiredSensitive classes
ticket_number (key)VARCHAR(15)YesDirect identifier
pnr_locatorVARCHAR(6)YesDirect identifier
traveller_idVARCHAR(11)YesDirect identifier
passenger_typeVARCHAR(3)Yes—
issued_atTIMESTAMPYes—
fare_amountDECIMAL(10,2)YesFinancial
statusVARCHAR(10)Yes—
FlightSegment — reservation.flight_segments · 10 columns

One flight coupon of a ticket: who flies where and when (an itinerary is a travel pattern).

ColumnTypeRequiredSensitive classes
segment_id (key)BIGINTYes—
ticket_numberVARCHAR(15)YesDirect identifier
coupon_numberINTYes—
flight_numberVARCHAR(6)Yes—
originVARCHAR(3)YesQuasi identifier
destinationVARCHAR(3)YesQuasi identifier
departure_dateDATEYes—
block_minutesINTYes—
cabinVARCHAR(8)Yes—
statusVARCHAR(10)Yes—
SpecialServiceRequest — reservation.special_service_requests · 6 columns

A special-service request (assistance, meal, minor, pet); it can reveal health or religion.

ColumnTypeRequiredSensitive classes
ssr_id (key)BIGINTYes—
pnr_locatorVARCHAR(6)YesDirect identifier
traveller_idVARCHAR(11)YesDirect identifier
ssr_codeVARCHAR(20)YesSensitive
free_textVARCHAR(200)NoSensitive, PHI
statusVARCHAR(10)Yes—
TravelDocument — departure_control.travel_documents · 9 columns

A passport-like travel document captured for advance passenger information.

ColumnTypeRequiredSensitive classes
document_id (key)BIGINTYes—
traveller_refVARCHAR(11)YesDirect identifier
document_typeVARCHAR(18)Yes—
document_numberVARCHAR(9)YesPII, Direct identifier
surname_on_documentVARCHAR(64)YesPII, Direct identifier
nationalityVARCHAR(2)YesPII, Quasi identifier
issuing_countryVARCHAR(2)Yes—
date_of_birthDATEYesPII, Quasi identifier
expiry_dateDATEYes—
CheckIn — departure_control.check_ins · 9 columns

A check-in and boarding record for one flight coupon.

ColumnTypeRequiredSensitive classes
checkin_id (key)BIGINTYes—
ticket_refVARCHAR(15)YesDirect identifier
segment_refBIGINTYes—
document_idBIGINTNo—
seatVARCHAR(4)No—
checked_bagsINTYes—
checked_in_atTIMESTAMPYes—
boarding_statusVARCHAR(16)Yes—
boarding_pass_dataVARCHAR(64)NoPII, Direct identifier
HotelStay — property.hotel_stays · 11 columns

A hotel stay in the property-management system.

ColumnTypeRequiredSensitive classes
stay_id (key)BIGINTYes—
confirmation_numberVARCHAR(11)YesDirect identifier
guest_refVARCHAR(11)YesDirect identifier
loyalty_refVARCHAR(12)NoDirect identifier
property_codeVARCHAR(6)Yes—
arrival_dateDATEYes—
departure_dateDATEYes—
room_typeVARCHAR(10)Yes—
statusVARCHAR(12)Yes—
nightly_rateDECIMAL(10,2)YesFinancial
guest_notesVARCHAR(200)NoSensitive, PHI
FolioCharge — property.folio_charges · 5 columns

A charge posted to a stay's folio (the running hotel bill).

ColumnTypeRequiredSensitive classes
charge_id (key)BIGINTYes—
stay_idBIGINTYes—
posted_onDATEYes—
categoryVARCHAR(14)Yes—
amountDECIMAL(10,2)YesFinancial
LoyaltyMember — loyalty.loyalty_members · 6 columns

A loyalty programme membership of a traveller.

ColumnTypeRequiredSensitive classes
member_number (key)VARCHAR(12)YesDirect identifier
traveller_refVARCHAR(11)YesDirect identifier
tierVARCHAR(10)Yes—
points_balanceBIGINTYesFinancial
enrolled_onDATEYes—
statusVARCHAR(10)Yes—
LoyaltyActivity — loyalty.loyalty_activities · 5 columns

A points movement: earned on a flight or stay, redeemed, expired or adjusted.

ColumnTypeRequiredSensitive classes
activity_id (key)BIGINTYes—
member_numberVARCHAR(12)YesDirect identifier
activity_typeVARCHAR(12)Yes—
pointsINTYes—
activity_dateDATEYes—
Payment — payments.payments · 11 columns

A card payment for a booking or a hotel stay (gateway token, never a card number).

ColumnTypeRequiredSensitive classes
payment_id (key)BIGINTYes—
pnr_refVARCHAR(6)NoDirect identifier
stay_confirmation_refVARCHAR(11)NoDirect identifier
card_tokenVARCHAR(24)YesPayment, Direct identifier
card_last4VARCHAR(4)NoPayment
cardholder_nameVARCHAR(96)NoPII, Direct identifier
amountDECIMAL(10,2)YesFinancial
currencyVARCHAR(3)Yes—
statusVARCHAR(10)Yes—
chargeback_reasonVARCHAR(24)No—
processed_atTIMESTAMPYes—

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.

ConnectorStatus with Travel, Airlines & Hospitality
Local files (Parquet / CSV)Verified with this pack
PostgreSQLVerified with this pack
Amazon S3 / S3-compatible storageCompatible 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 StorageCompatible by capability (not yet verified with this pack)
DatabricksCompatible by capability (not yet verified with this pack)
Generic SQL (SQLAlchemy)Compatible by capability (not yet verified with this pack)
Google BigQueryCompatible 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)
MariaDBCompatible by capability (not yet verified with this pack)
Microsoft SQL ServerCompatible by capability (not yet verified with this pack)
MongoDB (connector in preview)Compatible by capability (not yet verified with this pack)
MySQLCompatible by capability (not yet verified with this pack)
Oracle DatabaseCompatible by capability (not yet verified with this pack)
Parquet filesCompatible by capability (not yet verified with this pack)
SnowflakeCompatible 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 Travel, Airlines & Hospitality 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)

StepEstimate
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 Travel, Airlines & Hospitality 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

TRV_STRICT

Every gate; full masking coverage; zero orphaned bookings, tickets, segments, documents, check-ins, stays, folio charges, loyalty records or payments; exact row counts. Default for masked travel and hospitality 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

TRV_SCENARIO_TESTING

Every gate, with relaxed NULL-ratio drift for scenario datasets whose business states (denied boarding without a seat, stays without a loyalty membership, payments without a chargeback reason) 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 20%
  • 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 Travel, Airlines & Hospitality 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 Travel, Airlines & Hospitality pack or request a feature for it.

Downloads

Version 1.0.0, 68 files (237.4 KB), all synthetic and generated from the pack itself. Every file’s SHA-256 is listed in MANIFEST.json.

Start here (2)
Entity–relationship diagram (2)
Sample schemas (6)
Policy templates (9)
API, CLI, SDK and CI/CD examples (10)
Synthetic sample data (CSV, JSON, Parquet) (37)

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 Travel, Airlines & Hospitality 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 Travel, Airlines & Hospitality 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 Travel, Airlines & Hospitality 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 Travel, Airlines & Hospitality 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 Travel, Airlines & Hospitality pack include?
13 runnable scenarios (everyday travel, overbooked flight, irregular operations, special assistance and more), plus 1 negative-test scenario kept apart from valid data.
Do Travel, Airlines & Hospitality 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 Travel, Airlines & Hospitality 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 dataNotify 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.