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Data Use Agreement

DUA
technology·Updated Jun 23, 2026

Definition

ISO-11179 Definition

A legally binding contract governing the permitted uses and disclosures of a limited dataset shared between covered entities or research institutions, satisfying HIPAA Privacy Rule requirements under 45 CFR 164.514(e). In healthcare data engineering, DUAs define field-level restrictions, permissible analytics, and security controls applied to de-identified or limited datasets used in research or operational reporting workflows.

Standard Abbreviation

DUA

Category

technology

Production DDL — DIM_SYSTEM

DIM_SYSTEM.sql
CREATE OR REPLACE TABLE DIM_SYSTEM (
    sys_key         INTEGER       NOT NULL  -- surrogate key,
    sys_id          VARCHAR(50)   NOT NULL  -- system identifier,
    sys_nm          VARCHAR(200)  NOT NULL  -- system name,
    sys_type_cd     VARCHAR(50)             -- system type code,
    sys_vrsn        VARCHAR(50)             -- system version,
    vndr_nm         VARCHAR(200)            -- vendor name,
    intfc_type_cd   VARCHAR(50)             -- interface type code,
    intfc_proto_cd  VARCHAR(20)             -- interface protocol,
    env_cd          VARCHAR(20)             -- environment code,
    host_nm         VARCHAR(200)            -- hostname,
    ip_addr         VARCHAR(45)             -- IP address,
    sts_cd          VARCHAR(20)             -- status code,
    eff_dt          DATE          NOT NULL  -- effective date,
    exp_dt          DATE                    -- expiration date,
    rec_creat_dt    TIMESTAMP     NOT NULL  -- record created date
);

Standard Snowflake DDL for the canonical technology table. Convert to BigQuery or Databricks →

Why This Term Matters

Healthcare data terminology is foundational for any data engineer working in this industry. Precise understanding of standard terms enables accurate schema design, reduces downstream data quality issues, and ensures pipelines meet the regulatory and interoperability requirements imposed by HIPAA, HL7 FHIR, and CMS reporting frameworks. Without this foundation, even technically well-built pipelines produce data that fails validation when it reaches payers or regulators.

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