To guarantee trust, we’ve implemented a comprehensive, multi-layered validation pipeline that catches issues before they ever reach a dashboard or a patient-facing feature.

HaloDoc is a healthcare technology platform that offers telemedicine services, online pharmacy, and health information resources to users in Indonesia. Through its platform, HaloDoc aims to improve access to healthcare services, empower patients to take control of their health, and facilitate collaboration between healthcare providers. Developers can learn from HaloDoc's innovative use of technology to address healthcare challenges, leveraging telemedicine, artificial intelligence, and data analytics to enhance patient care and outcomes.

Halodoc

Halodoc implemented a four-layer data validation pipeline to ensure accuracy across their lakehouse architecture. Layer 1 validates RDS-to-Data Lake ingestion using time-bound queries to handle pipeline latency. Layer 2 uses AI-generated queries to verify structural integrity between Data Lake and Redshift. Layer 3 enforces business rules in the presentation layer. Layer 4 reconciles data pushed to internal RDS systems using configuration-driven mapping tables. The approach combines automated validation with AI-assisted query generation, integrated monitoring via Metabase, and real-time alerting to catch data quality issues before they impact analytics or patient-facing features.

Halodoc’s Layered Data Validation Strategy

Layer 1: The "Pulse Check" (RDS vs. Data Lake)

Layer 2: AI-Assisted Structural Validation (Processed → DWH)

Layer 3: AI-Assisted Warehouse Verification (DWH → PL)

Layer 4: Data Reconciliation / Internal Team Validation