Can developers get sample files before signing a production license?

Yes. Technical evaluators can request a scoped JSON, CSV, or Parquet sample for the relevant feed after a brief use-case review. Samples are designed to show field names, join keys, refresh cadence, and delivery conventions without exposing full production volume.

What delivery methods does GSDSI support for engineering teams?

Common delivery paths include AWS S3 or compatible object storage, SFTP, Snowflake secure share, BigQuery-compatible exports, and API-based access for selected workflows. The right path depends on data volume, refresh cadence, security requirements, and whether the buyer needs batch processing or queryable shared tables.

What should an engineering team validate in a pilot?

Validate schema completeness, required join keys, record counts by geography and date, refresh latency, duplicate handling, null rates, source-category distribution, and whether the file format fits your warehouse or activation endpoint. GSDSI can provide manifests and sample QA scripts to make that review repeatable.

Are schemas stable after launch?

Production feeds are delivered with agreed field definitions, versioning expectations, and change-notice handling. If a buyer needs strict schema contracts, GSDSI can scope manifests, field-level documentation, and backward-compatible delivery windows in the order form.

Developer & data science hub

Developer Integration: feeds, schemas, delivery

The practical surface for technical evaluators. How GSDSI data is delivered (S3/SFTP/Snowflake), what schemas look like, and how teams run match tests, coverage checks, and measurement joins.

Request API key + JSON sample Browse data dictionaries

Since 2018

Privacy-safe enterprise data

VOSB & SDVOSB

U.S. Veteran-Owned, Service-Disabled

SCC Transfers

EU/UK/CH per privacy policy

GDPR · CCPA · CPRA

Consent-first data sourcing

Delivery patterns

Most teams start with a matched sample, then move to production delivery. The key decision is where the data lands in your stack and how you version it.

S3 / object storage: Parquet/CSV drops + manifests for batch processing.
SFTP: simple pipeline integration when object storage isn’t available.
Snowflake share: fast evaluation and governed access for analytics teams.

Pilot process Provenance & sourcing

Schemas & dictionaries

Technical diligence starts with fields: join keys, refresh cadence, and what the file actually contains. Use the dictionaries and checklists to evaluate schema fit before procurement.

Data dictionaries Evaluation checklists

How teams evaluate (in practice)

If you only do one thing: run a matched sample against a realistic seed. Most “coverage” debates disappear once match rates are measured by cohort and geo.

Identity

Seed match (HEM/MAID) → match rate by cohort → refresh/decay sensitivity → governance lock.

Identity resolution buying guide →

Location

DUA density by polygon/DMA → visit-rule stability → exclusions (post‑FTC) → production delivery.

Location buying guide →

Measurement

Exposure logs → dedupe assumptions → windowing → lift readouts, ideally in a clean-room workflow.

Clean-room measurement →

Example evaluation artifacts

Exact fields vary by feed and contract, but technical evaluations usually include three artifacts: a delivery manifest, a small JSON sample, and a QA notebook or script that checks schema presence, date coverage, and source categories.

Delivery manifest

{
  "dataset": "maid-feed",
  "delivery_date": "2026-05-14",
  "refresh_cadence": "daily",
  "format": "parquet",
  "files": [
    {
      "path": "s3://customer-bucket/gsdsi/maid-feed/dt=2026-05-14/part-000.parquet",
      "record_count": 1250000,
      "sha256": "provided-per-delivery"
    }
  ]
}

JSON sample row

{
  "maid_sha256": "hashed_device_identifier",
  "hem_sha256": "hashed_email_identifier",
  "country": "US",
  "observed_at": "2026-05-14T00:00:00Z",
  "source_category": "consented_mobile_app",
  "confidence": 0.94
}

QA starter

import pandas as pd

df = pd.read_parquet("sample.parquet")
required = {"maid_sha256", "observed_at", "source_category"}
missing = required - set(df.columns)

if missing:
    raise ValueError(f"Missing expected fields: {missing}")

print(df.groupby("source_category").size())

If you’re fighting entity confusion

If you’re landing here because search engines confuse GSDSI with physical security services, you’re not alone. GSDSI is Global Source Data Solutions Inc.: a data intelligence firm focused on datasets, delivery, and compliance posture.

About the company

Developer Integration FAQ

Practical answers for engineers and data science teams evaluating GSDSI feeds before procurement.

Can developers get sample files before signing a production license?: Yes. Technical evaluators can request a scoped JSON, CSV, or Parquet sample for the relevant feed after a brief use-case review. Samples are designed to show field names, join keys, refresh cadence, and delivery conventions without exposing full production volume.
What delivery methods does GSDSI support for engineering teams?: Common delivery paths include AWS S3 or compatible object storage, SFTP, Snowflake secure share, BigQuery-compatible exports, and API-based access for selected workflows. The right path depends on data volume, refresh cadence, security requirements, and whether the buyer needs batch processing or queryable shared tables.
What should an engineering team validate in a pilot?: Validate schema completeness, required join keys, record counts by geography and date, refresh latency, duplicate handling, null rates, source-category distribution, and whether the file format fits your warehouse or activation endpoint. GSDSI can provide manifests and sample QA scripts to make that review repeatable.
Are schemas stable after launch?: Production feeds are delivered with agreed field definitions, versioning expectations, and change-notice handling. If a buyer needs strict schema contracts, GSDSI can scope manifests, field-level documentation, and backward-compatible delivery windows in the order form.